├── .gitignore ├── LICENSE ├── README.md ├── config.yaml ├── dataset ├── README.md └── ml-1m │ ├── README.md │ ├── ml-1m.inter │ ├── ml-1m.item │ └── ml-1m.user ├── environment.yaml ├── mamba4rec.py └── run.py /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__/ 2 | log/ 3 | log_tensorboard/ 4 | # dataset/ 5 | amazon-*/ 6 | saved/ 7 | *.pth 8 | *.pyc 9 | .DS_Store 10 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Chengkai Liu 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Mamba4Rec 2 | 3 | > **Mamba4Rec: Towards Efficient Sequential Recommendation with Selective State Space Models (RelKD@KDD 2024 Best Paper Award)**\ 4 | > Chengkai Liu, Jianghao Lin, Jianling Wang, Hanzhou Liu, James Caverlee\ 5 | > Paper: https://arxiv.org/abs/2403.03900 6 | 7 | ## Usage 8 | 9 | ### Requirements 10 | 11 | * Python 3.7+ 12 | * PyTorch 1.12+ 13 | * CUDA 11.6+ 14 | * Install RecBole: 15 | * `pip install recbole` 16 | * Install causal Conv1d and the core Mamba package: 17 | * `pip install causal-conv1d>=1.2.0` 18 | * `pip install mamba-ssm` 19 | 20 | You can also refer to the required environment specifications in `environment.yaml`. 21 | 22 | ### Run 23 | 24 | ```python run.py``` 25 | 26 | 27 | Specifying the dataset in `config.yaml` will trigger an automatic download. Please set an appropriate maximum sequence length in `config.yaml` for each dataset before training. 28 | 29 | 30 | ## Citation 31 | ```bibtex 32 | @article{liu2024mamba4rec, 33 | title={Mamba4rec: Towards efficient sequential recommendation with selective state space models}, 34 | author={Liu, Chengkai and Lin, Jianghao and Wang, Jianling and Liu, Hanzhou and Caverlee, James}, 35 | journal={arXiv preprint arXiv:2403.03900}, 36 | year={2024} 37 | } 38 | ``` 39 | 40 | 41 | ## Acknowledgment 42 | 43 | This project is based on [Mamba](https://github.com/state-spaces/mamba), [Causal-Conv1d](https://github.com/Dao-AILab/causal-conv1d), and [RecBole](https://github.com/RUCAIBox/RecBole). Thanks for their excellent works. 44 | -------------------------------------------------------------------------------- /config.yaml: -------------------------------------------------------------------------------- 1 | gpu_id: '0' 2 | log_wandb: False 3 | 4 | # mamba4rec settings 5 | hidden_size: 64 # (int) Number of features in the hidden state. 6 | num_layers: 1 # (int) Number of Mamba layers. 7 | dropout_prob: 0.2 # (float) Dropout rate. 8 | loss_type: 'CE' # (str) Type of loss function. Range in ['BPR', 'CE']. 9 | 10 | d_state: 32 # (int) SSM state expansion factor 11 | d_conv: 4 # (int) Local convolution width 12 | expand: 2 # (int) Block expansion factor 13 | 14 | # dataset settings 15 | dataset: ml-1m 16 | MAX_ITEM_LIST_LENGTH: 200 # 200 for MovieLens-1M 17 | 18 | # dataset: amazon-beauty 19 | # dataset: amazon-video-games 20 | # MAX_ITEM_LIST_LENGTH: 50 # 50 for Amazon datasets 21 | 22 | USER_ID_FIELD: user_id 23 | ITEM_ID_FIELD: item_id 24 | load_col: 25 | inter: [user_id, item_id, timestamp] 26 | 27 | user_inter_num_interval: "[5,inf)" 28 | item_inter_num_interval: "[5,inf)" 29 | 30 | # training settings 31 | epochs: 300 32 | train_batch_size: 2048 33 | learner: adam 34 | learning_rate: 0.001 35 | eval_step: 1 36 | stopping_step: 10 37 | train_neg_sample_args: ~ 38 | 39 | # evalution settings 40 | metrics: ['Hit', 'NDCG', 'MRR'] 41 | valid_metric: NDCG@10 42 | eval_batch_size: 4096 43 | weight_decay: 0.0 44 | topk: [10] 45 | -------------------------------------------------------------------------------- /dataset/README.md: -------------------------------------------------------------------------------- 1 | # Datasets 2 | 3 | Specifying the dataset in `config.yaml` will trigger an automatic download by [RecBole](https://github.com/RUCAIBox/RecSysDatasets). 4 | 5 | RecBole can directly download MovieLens and Amazon datasets. For other datasets, conversions into RecBole's atomic file format are required. 6 | -------------------------------------------------------------------------------- /dataset/ml-1m/README.md: -------------------------------------------------------------------------------- 1 | INTERACTIONs DATASET FILE DESCRIPTION 2 | ------------------------------------------------------------------------------------ 3 | ------------------------------------------------------------------------------------ 4 | The file ml-1m.inter comprising the ratings of users over the movies. 5 | Each record/line in the file has the following fields: user_id, item_id, rating, timestamp 6 | 7 | user_id: the id of the users and its type is token. 8 | item_id: the id of the movies and its type is token. 9 | rating: the rating of the users over the movies, and its type is float. 10 | timestamp: the UNIX timestamp of the rating, and its type is float. 11 | 12 | MOVIES INFORMATION DATASET FILE DESCRIPTION 13 | ------------------------------------------------------------------------------------ 14 | ------------------------------------------------------------------------------------ 15 | The file ml-1m.item comprising the attributes of the movies. 16 | Each record/line in the file has the following fields: item_id, movie_title, release_year, genre 17 | 18 | item_id: the id of the movies and its type is token. 19 | movie_title: the title of the movies, and its type is token_seq. 20 | release_year: the year when movies were released, and its type is float. 21 | genre: the genres of the movies, and its type is token_seq. 22 | 23 | 24 | USERS INFORMATION DATASET FILE DESCRIPTION 25 | ------------------------------------------------------------------------------------ 26 | ------------------------------------------------------------------------------------ 27 | The file ml-1m.user comprising the attributes of the users. 28 | Each record/line in the file has the following fields: user_id, age, gender, occupation and zip_code 29 | 30 | user_id: the id of the users and its type is token. 31 | age: the age of the users, and its type is float. 32 | gender: the gender of the users, and its type is token. 33 | occupation: the occupation of the users, and its type is token. 34 | zip_code: the zip_code of the users, and its type is token. -------------------------------------------------------------------------------- /dataset/ml-1m/ml-1m.user: -------------------------------------------------------------------------------- 1 | user_id:token age:token gender:token occupation:token zip_code:token 2 | 1 1 F 10 48067 3 | 2 56 M 16 70072 4 | 3 25 M 15 55117 5 | 4 45 M 7 02460 6 | 5 25 M 20 55455 7 | 6 50 F 9 55117 8 | 7 35 M 1 06810 9 | 8 25 M 12 11413 10 | 9 25 M 17 61614 11 | 10 35 F 1 95370 12 | 11 25 F 1 04093 13 | 12 25 M 12 32793 14 | 13 45 M 1 93304 15 | 14 35 M 0 60126 16 | 15 25 M 7 22903 17 | 16 35 F 0 20670 18 | 17 50 M 1 95350 19 | 18 18 F 3 95825 20 | 19 1 M 10 48073 21 | 20 25 M 14 55113 22 | 21 18 M 16 99353 23 | 22 18 M 15 53706 24 | 23 35 M 0 90049 25 | 24 25 F 7 10023 26 | 25 18 M 4 01609 27 | 26 25 M 7 23112 28 | 27 25 M 11 19130 29 | 28 25 F 1 14607 30 | 29 35 M 7 33407 31 | 30 35 F 7 19143 32 | 31 56 M 7 06840 33 | 32 25 F 0 19355 34 | 33 45 M 3 55421 35 | 34 18 F 0 02135 36 | 35 45 M 1 02482 37 | 36 25 M 3 94123 38 | 37 25 F 9 66212 39 | 38 18 F 4 02215 40 | 39 18 M 4 61820 41 | 40 45 M 0 10543 42 | 41 18 F 4 15116 43 | 42 25 M 8 24502 44 | 43 25 M 12 60614 45 | 44 45 M 17 98052 46 | 45 45 F 16 94110 47 | 46 18 M 19 75602 48 | 47 18 M 4 94305 49 | 48 25 M 4 92107 50 | 49 18 M 12 77084 51 | 50 25 F 2 98133 52 | 51 1 F 10 10562 53 | 52 18 M 4 72212 54 | 53 25 M 0 96931 55 | 54 50 M 1 56723 56 | 55 35 F 12 55303 57 | 56 35 M 20 60440 58 | 57 18 M 19 30350 59 | 58 25 M 2 30303 60 | 59 50 F 1 55413 61 | 60 50 M 1 72118 62 | 61 25 M 17 95122 63 | 62 35 F 3 98105 64 | 63 18 M 4 54902 65 | 64 18 M 1 53706 66 | 65 35 M 12 55803 67 | 66 25 M 18 57706 68 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3 50311 2739 | 2738 50 F 2 22181 2740 | 2739 35 F 7 92316 2741 | 2740 45 M 17 22181 2742 | 2741 25 M 14 60614 2743 | 2742 25 M 17 36092 2744 | 2743 25 M 14 72601 2745 | 2744 18 M 17 53818 2746 | 2745 35 F 1 30345 2747 | 2746 50 M 14 50265-2850 2748 | 2747 18 F 4 90025 2749 | 2748 25 M 4 85719 2750 | 2749 25 M 12 10025 2751 | 2750 25 F 15 91360 2752 | 2751 25 F 16 28120 2753 | 2752 25 M 15 77006 2754 | 2753 50 F 20 27516 2755 | 2754 50 M 12 60148 2756 | 2755 35 M 7 29631 2757 | 2756 18 M 4 97331 2758 | 2757 25 M 2 10023 2759 | 2758 56 M 8 12436 2760 | 2759 25 M 7 36102 2761 | 2760 56 M 0 01886 2762 | 2761 35 M 6 80917 2763 | 2762 50 M 16 63131 2764 | 2763 18 F 4 48104 2765 | 2764 25 M 7 91403 2766 | 2765 50 M 7 02420 2767 | 2766 45 M 7 55016 2768 | 2767 25 M 0 99163 2769 | 2768 25 M 17 90210 2770 | 2769 25 M 7 20170 2771 | 2770 35 M 1 14260 2772 | 2771 18 M 1 92037 2773 | 2772 35 M 17 10025 2774 | 2773 35 M 2 78766 2775 | 2774 25 M 1 92103 2776 | 2775 18 F 4 90038 2777 | 2776 25 M 14 22205 2778 | 2777 18 M 4 95326 2779 | 2778 56 M 13 94025 2780 | 2779 35 M 7 10040 2781 | 2780 25 M 17 94133 2782 | 2781 35 M 4 45205 2783 | 2782 18 F 4 48858 2784 | 2783 45 M 17 78750 2785 | 2784 50 M 20 20007 2786 | 2785 56 M 13 29928 2787 | 2786 25 M 4 60201 2788 | 2787 25 M 4 60614 2789 | 2788 56 M 1 90049 2790 | 2789 18 M 4 90025 2791 | 2790 25 M 20 85013 2792 | 2791 56 M 7 10023 2793 | 2792 18 M 4 02452 2794 | 2793 35 M 7 70116 2795 | 2794 25 M 2 20036 2796 | 2795 50 M 13 33710 2797 | 2796 25 M 14 92104 2798 | 2797 25 F 7 94043 2799 | 2798 35 M 20 10038 2800 | 2799 18 M 0 55404 2801 | 2800 18 F 4 73070 2802 | 2801 25 F 3 12866 2803 | 2802 35 M 20 10036 2804 | 2803 25 M 7 94022 2805 | 2804 35 F 0 46234 2806 | 2805 56 M 19 06040 2807 | 2806 1 M 10 15208 2808 | 2807 35 F 11 22043 2809 | 2808 56 M 16 60610 2810 | 2809 25 M 7 10016 2811 | 2810 1 F 10 64093 2812 | 2811 25 M 19 94122 2813 | 2812 25 M 17 60607 2814 | 2813 25 M 20 55406 2815 | 2814 35 M 7 02127 2816 | 2815 35 M 17 60477 2817 | 2816 25 M 7 60015 2818 | 2817 35 M 12 53130 2819 | 2818 25 M 12 55106 2820 | 2819 45 M 14 44122 2821 | 2820 35 F 0 02138 2822 | 2821 18 M 20 10036 2823 | 2822 50 M 7 10003 2824 | 2823 25 M 4 02452 2825 | 2824 18 M 4 02111 2826 | 2825 25 F 20 94014 2827 | 2826 35 M 12 10011 2828 | 2827 25 M 17 94063 2829 | 2828 18 M 4 02454 2830 | 2829 45 M 20 10013 2831 | 2830 35 M 7 94506 2832 | 2831 25 M 0 32225 2833 | 2832 25 M 17 20036 2834 | 2833 25 M 15 85003 2835 | 2834 35 M 1 33304 2836 | 2835 35 M 20 60622 2837 | 2836 25 M 12 07974 2838 | 2837 18 M 0 49506 2839 | 2838 18 M 1 95864 2840 | 2839 25 M 17 10003 2841 | 2840 25 M 0 60660 2842 | 2841 50 M 12 98056 2843 | 2842 35 M 17 30145 2844 | 2843 25 F 4 78705 2845 | 2844 25 M 20 87103 2846 | 2845 25 M 4 74074 2847 | 2846 18 F 4 91604 2848 | 2847 18 M 4 10027 2849 | 2848 25 M 0 47401 2850 | 2849 45 M 11 94114 2851 | 2850 18 M 20 94608 2852 | 2851 25 F 7 94107 2853 | 2852 25 M 12 98037 2854 | 2853 1 M 1 444555 2855 | 2854 18 F 4 64151 2856 | 2855 25 M 4 94102 2857 | 2856 35 M 11 22030 2858 | 2857 25 M 0 10469 2859 | 2858 1 F 10 72601 2860 | 2859 18 M 1 95814 2861 | 2860 35 M 17 20009 2862 | 2861 25 M 15 06510 2863 | 2862 45 M 7 55066 2864 | 2863 25 F 2 27705 2865 | 2864 35 F 3 11725 2866 | 2865 25 F 6 21014 2867 | 2866 25 M 7 53717 2868 | 2867 45 M 17 15204 2869 | 2868 35 M 12 94025 2870 | 2869 50 M 17 07650 2871 | 2870 50 M 18 95472 2872 | 2871 25 F 14 98110 2873 | 2872 25 M 20 94014 2874 | 2873 18 F 4 64093 2875 | 2874 35 F 20 20036 2876 | 2875 25 M 2 10013 2877 | 2876 25 M 17 02149 2878 | 2877 35 M 1 80687 2879 | 2878 50 F 20 88005 2880 | 2879 18 M 4 60062 2881 | 2880 18 M 12 55405 2882 | 2881 25 F 12 77019 2883 | 2882 18 M 20 78759 2884 | 2883 18 M 20 30101 2885 | 2884 50 F 3 53222 2886 | 2885 18 F 1 80302 2887 | 2886 18 M 4 60073 2888 | 2887 25 M 3 66205 2889 | 2888 25 M 14 55438 2890 | 2889 35 M 3 55104 2891 | 2890 35 M 7 90069 2892 | 2891 18 F 4 66044 2893 | 2892 25 M 4 84064 2894 | 2893 25 M 1 94025 2895 | 2894 18 M 4 44130 2896 | 2895 25 M 11 07039 2897 | 2896 18 M 14 60073 2898 | 2897 25 M 5 97405 2899 | 2898 18 M 4 02453 2900 | 2899 35 M 14 94133 2901 | 2900 18 M 12 95120 2902 | 2901 25 M 17 78749 2903 | 2902 25 M 12 98007 2904 | 2903 25 M 16 20850 2905 | 2904 25 M 4 16802 2906 | 2905 35 M 17 55407 2907 | 2906 35 M 19 55421 2908 | 2907 35 F 5 12345 2909 | 2908 18 M 2 74403 2910 | 2909 25 M 7 43228 2911 | 2910 1 M 19 75602 2912 | 2911 25 F 4 55455 2913 | 2912 18 F 4 48104 2914 | 2913 35 F 20 98119 2915 | 2914 25 M 4 92614 2916 | 2915 25 M 7 23423 2917 | 2916 45 M 2 98011 2918 | 2917 25 M 0 60201 2919 | 2918 25 M 12 98112 2920 | 2919 18 M 17 88076 2921 | 2920 25 M 2 55116 2922 | 2921 50 M 1 53221 2923 | 2922 25 M 17 52402 2924 | 2923 35 M 12 44256 2925 | 2924 18 F 4 94121 2926 | 2925 25 F 4 23454 2927 | 2926 18 F 2 55118 2928 | 2927 25 M 12 24060 2929 | 2928 25 M 2 90068 2930 | 2929 18 M 2 60614 2931 | 2930 50 M 17 45420 2932 | 2931 18 M 4 16803 2933 | 2932 25 M 12 55346 2934 | 2933 35 M 11 32082 2935 | 2934 35 F 20 91324 2936 | 2935 25 M 0 55126 2937 | 2936 18 F 4 94122 2938 | 2937 18 F 4 48103 2939 | 2938 25 F 0 55345 2940 | 2939 25 F 15 94901 2941 | 2940 25 M 0 02143 2942 | 2941 35 M 12 98033 2943 | 2942 50 F 1 93103 2944 | 2943 35 M 12 95864 2945 | 2944 18 F 4 48104 2946 | 2945 35 F 17 89507 2947 | 2946 35 M 16 92630 2948 | 2947 25 F 0 02138 2949 | 2948 18 M 14 48185 2950 | 2949 25 M 17 98033 2951 | 2950 18 M 0 72761 2952 | 2951 35 F 14 94303 2953 | 2952 25 M 4 06511 2954 | 2953 25 M 20 23225 2955 | 2954 18 M 2 11435 2956 | 2955 35 M 14 92025 2957 | 2956 25 M 12 50265 2958 | 2957 18 M 4 48104 2959 | 2958 25 M 7 91911 2960 | 2959 35 M 17 06074 2961 | 2960 18 M 4 48104 2962 | 2961 50 M 17 91977 2963 | 2962 35 M 3 94109 2964 | 2963 18 F 4 48104 2965 | 2964 25 M 17 07030 2966 | 2965 35 F 4 48103 2967 | 2966 25 F 4 48104 2968 | 2967 25 M 0 94110 2969 | 2968 18 F 0 48104 2970 | 2969 25 M 7 06851 2971 | 2970 18 M 4 48104 2972 | 2971 25 M 4 48104 2973 | 2972 25 M 4 97330 2974 | 2973 18 F 4 48108 2975 | 2974 18 M 4 48105 2976 | 2975 35 M 11 20879 2977 | 2976 18 M 20 86001 2978 | 2977 18 F 4 48104 2979 | 2978 35 M 20 45236 2980 | 2979 35 M 2 45243 2981 | 2980 45 M 7 91766 2982 | 2981 45 M 6 52556 2983 | 2982 25 M 4 54902 2984 | 2983 18 M 4 97850 2985 | 2984 25 M 4 48105 2986 | 2985 18 M 4 97333 2987 | 2986 25 M 4 24060 2988 | 2987 18 M 4 15213 2989 | 2988 18 F 6 92122 2990 | 2989 35 M 12 97330 2991 | 2990 35 M 12 45243 2992 | 2991 50 M 1 48105 2993 | 2992 18 M 4 14853 2994 | 2993 18 M 19 12133 2995 | 2994 35 M 5 95008 2996 | 2995 25 M 15 97333 2997 | 2996 18 M 0 63011 2998 | 2997 35 F 16 45238 2999 | 2998 25 F 1 45241 3000 | 2999 25 M 7 33813 3001 | 3000 25 M 4 55408 3002 | 3001 18 M 4 94704 3003 | 3002 45 M 11 90028 3004 | 3003 18 M 15 94606 3005 | 3004 18 F 4 02138 3006 | 3005 25 M 17 94133 3007 | 3006 18 M 4 45750 3008 | 3007 35 M 0 33174 3009 | 3008 18 M 4 94403 3010 | 3009 35 F 3 55112 3011 | 3010 25 M 12 78759 3012 | 3011 25 M 1 10003 3013 | 3012 25 M 12 78660 3014 | 3013 45 M 17 97124 3015 | 3014 35 M 7 06430 3016 | 3015 56 M 6 62707 3017 | 3016 18 M 12 78759 3018 | 3017 35 F 9 85255 3019 | 3018 35 M 0 45242 3020 | 3019 50 M 12 97330 3021 | 3020 35 F 7 60302 3022 | 3021 45 F 15 78750 3023 | 3022 25 M 17 78730 3024 | 3023 25 F 7 92108 3025 | 3024 35 M 17 14302 3026 | 3025 35 F 0 90004 3027 | 3026 18 F 4 45750 3028 | 3027 18 M 4 85719 3029 | 3028 1 M 10 12701 3030 | 3029 18 M 4 92037 3031 | 3030 35 M 1 48103 3032 | 3031 18 M 4 48135 3033 | 3032 25 M 0 47303 3034 | 3033 18 M 4 02906 3035 | 3034 18 F 1 33143 3036 | 3035 25 F 7 90230 3037 | 3036 56 M 13 14843 3038 | 3037 25 F 2 97333 3039 | 3038 35 F 2 97401 3040 | 3039 50 F 3 33611 3041 | 3040 25 M 8 22046 3042 | 3041 25 M 4 97333 3043 | 3042 56 M 1 49042 3044 | 3043 18 M 2 90069 3045 | 3044 35 M 16 77301 3046 | 3045 45 M 1 90631 3047 | 3046 1 M 4 46311 3048 | 3047 25 F 4 94117 3049 | 3048 18 M 2 02066 3050 | 3049 50 F 0 02421 3051 | 3050 35 M 12 94025 3052 | 3051 18 M 17 94063 3053 | 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| 3131 25 F 2 90036 3133 | 3132 35 M 7 98467 3134 | 3133 18 M 4 33720 3135 | 3134 18 M 4 55455 3136 | 3135 25 M 15 12345 3137 | 3136 25 F 4 72801 3138 | 3137 25 M 11 11217 3139 | 3138 35 M 17 46229 3140 | 3139 56 M 13 89128 3141 | 3140 25 F 4 78728 3142 | 3141 25 M 14 81679 3143 | 3142 25 M 20 85719 3144 | 3143 50 F 16 90272 3145 | 3144 25 F 7 85260 3146 | 3145 18 M 4 02138 3147 | 3146 25 M 0 31605 3148 | 3147 25 M 14 49355 3149 | 3148 18 M 20 72837 3150 | 3149 18 M 4 92602 3151 | 3150 25 F 3 92831 3152 | 3151 25 F 0 92870 3153 | 3152 18 M 4 61801 3154 | 3153 25 F 4 73160 3155 | 3154 25 F 4 95616 3156 | 3155 25 M 7 55112 3157 | 3156 35 M 7 07663 3158 | 3157 25 M 12 92880 3159 | 3158 25 M 15 15217 3160 | 3159 25 F 15 15213 3161 | 3160 25 M 17 77521 3162 | 3161 25 M 7 55408 3163 | 3162 25 M 1 08854-3115 3164 | 3163 18 M 15 95616 3165 | 3164 18 M 12 55118 3166 | 3165 35 M 7 60611 3167 | 3166 18 M 4 32607 3168 | 3167 25 M 2 77056 3169 | 3168 35 F 5 94546 3170 | 3169 50 M 0 23518 3171 | 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5528 | 5527 25 M 1 70803 5529 | 5528 50 M 1 45840 5530 | 5529 18 M 6 05273 5531 | 5530 18 F 4 63141 5532 | 5531 50 M 17 09056 5533 | 5532 25 M 17 27408 5534 | 5533 50 F 2 10025 5535 | 5534 45 M 16 30102 5536 | 5535 25 M 15 94538 5537 | 5536 25 M 16 67204 5538 | 5537 56 F 1 10543 5539 | 5538 45 M 12 55102 5540 | 5539 25 F 20 91403 5541 | 5540 35 M 1 19149 5542 | 5541 56 M 7 93924 5543 | 5542 45 M 7 11576 5544 | 5543 25 M 17 97401 5545 | 5544 25 M 15 08876 5546 | 5545 35 F 16 98072 5547 | 5546 18 M 14 26301 5548 | 5547 18 F 4 10025 5549 | 5548 18 F 4 02138 5550 | 5549 50 F 9 76034 5551 | 5550 35 M 15 48187 5552 | 5551 35 F 20 98103 5553 | 5552 50 M 7 23233 5554 | 5553 35 M 3 29425 5555 | 5554 25 M 1 92116 5556 | 5555 1 M 10 37830 5557 | 5556 45 M 6 92103 5558 | 5557 35 M 14 33701 5559 | 5558 1 M 10 02446 5560 | 5559 50 M 1 77706 5561 | 5560 35 F 17 98036 5562 | 5561 25 F 3 74075 5563 | 5562 35 F 1 54650 5564 | 5563 25 F 7 11753 5565 | 5564 45 M 20 02446 5566 | 5565 18 M 17 02114 5567 | 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5921 25 M 6 15146 5923 | 5922 56 M 3 94561 5924 | 5923 25 M 7 05401 5925 | 5924 35 M 0 55417 5926 | 5925 25 F 0 90035-4444 5927 | 5926 50 F 7 65712 5928 | 5927 35 M 14 10003 5929 | 5928 45 M 1 14760 5930 | 5929 35 F 0 33607 5931 | 5930 35 F 17 78681 5932 | 5931 18 F 7 94111 5933 | 5932 18 M 14 90038 5934 | 5933 25 M 2 98227 5935 | 5934 25 M 14 77075 5936 | 5935 18 M 0 35115 5937 | 5936 18 M 4 37130 5938 | 5937 25 M 12 60622 5939 | 5938 25 M 1 35401 5940 | 5939 45 F 15 77040 5941 | 5940 50 M 12 03062 5942 | 5941 25 F 0 60640 5943 | 5942 45 M 1 01867 5944 | 5943 45 F 1 19806 5945 | 5944 18 F 10 27606 5946 | 5945 35 F 2 35229 5947 | 5946 56 F 1 55406 5948 | 5947 45 F 16 97215 5949 | 5948 56 M 13 12124 5950 | 5949 18 M 17 47901 5951 | 5950 25 M 4 19713 5952 | 5951 18 M 4 77005 5953 | 5952 45 F 1 78231 5954 | 5953 1 M 10 21030 5955 | 5954 45 M 11 70802 5956 | 5955 25 F 7 34952 5957 | 5956 18 F 4 02142 5958 | 5957 18 M 20 02038 5959 | 5958 45 M 14 94301 5960 | 5959 18 F 4 14850 5961 | 5960 45 F 0 70460 5962 | 5961 45 F 0 78757 5963 | 5962 35 M 7 80221 5964 | 5963 25 M 15 02140 5965 | 5964 18 M 5 97202 5966 | 5965 25 M 17 77449 5967 | 5966 35 F 9 10021 5968 | 5967 50 M 16 73069-5429 5969 | 5968 56 M 7 98103 5970 | 5969 45 M 13 77706 5971 | 5970 35 M 11 55438 5972 | 5971 35 M 7 49504 5973 | 5972 25 F 20 55428 5974 | 5973 1 M 10 54701 5975 | 5974 25 F 1 32303 5976 | 5975 25 M 14 55104 5977 | 5976 25 F 1 26542 5978 | 5977 35 M 1 90012 5979 | 5978 35 M 1 49307 5980 | 5979 25 M 1 43214 5981 | 5980 56 M 1 42503 5982 | 5981 35 M 7 01776 5983 | 5982 35 M 1 56082 5984 | 5983 25 M 3 29208 5985 | 5984 25 M 1 60657 5986 | 5985 18 F 4 78705-5221 5987 | 5986 56 F 1 01060 5988 | 5987 25 M 16 10003 5989 | 5988 25 M 15 94022 5990 | 5989 1 F 10 74114 5991 | 5990 25 F 20 90046 5992 | 5991 35 F 20 94025 5993 | 5992 18 F 4 21046 5994 | 5993 50 F 20 50010 5995 | 5994 35 M 1 32501 5996 | 5995 35 F 1 14618 5997 | 5996 25 F 0 87114 5998 | 5997 25 F 7 10016 5999 | 5998 18 M 4 61820 6000 | 5999 25 F 1 55455 6001 | 6000 45 M 17 30075 6002 | 6001 25 F 7 94117 6003 | 6002 50 M 0 43231 6004 | 6003 45 F 17 78722 6005 | 6004 25 F 15 28401 6006 | 6005 25 F 5 28401 6007 | 6006 1 F 0 01036 6008 | 6007 35 M 17 80537 6009 | 6008 18 M 4 78705 6010 | 6009 25 F 12 60540 6011 | 6010 35 M 0 79606 6012 | 6011 35 M 15 80538 6013 | 6012 35 M 15 02871 6014 | 6013 25 F 20 32301 6015 | 6014 45 M 1 80634 6016 | 6015 25 F 9 80013 6017 | 6016 45 M 1 37209 6018 | 6017 35 F 7 21117 6019 | 6018 35 M 1 48906 6020 | 6019 25 M 0 10024 6021 | 6020 50 M 16 10023 6022 | 6021 25 M 12 08876 6023 | 6022 25 M 17 57006 6024 | 6023 25 M 0 43213 6025 | 6024 25 M 12 53705 6026 | 6025 25 F 1 32607 6027 | 6026 35 M 6 11210 6028 | 6027 18 M 4 20742 6029 | 6028 18 M 4 94133 6030 | 6029 25 F 1 23185 6031 | 6030 25 M 17 32618 6032 | 6031 18 F 0 45123 6033 | 6032 45 M 7 55108 6034 | 6033 50 M 13 78232 6035 | 6034 25 M 14 94117 6036 | 6035 25 F 1 78734 6037 | 6036 25 F 15 32603 6038 | 6037 45 F 1 76006 6039 | 6038 56 F 1 14706 6040 | 6039 45 F 0 01060 6041 | 6040 25 M 6 11106 6042 | -------------------------------------------------------------------------------- /environment.yaml: -------------------------------------------------------------------------------- 1 | name: mamba4rec 2 | channels: 3 | - defaults 4 | dependencies: 5 | - _libgcc_mutex=0.1=main 6 | - _openmp_mutex=5.1=1_gnu 7 | - ca-certificates=2023.12.12=h06a4308_0 8 | - certifi=2024.2.2=py38h06a4308_0 9 | - ld_impl_linux-64=2.38=h1181459_1 10 | - libffi=3.4.4=h6a678d5_0 11 | - libgcc-ng=11.2.0=h1234567_1 12 | - libgomp=11.2.0=h1234567_1 13 | - libstdcxx-ng=11.2.0=h1234567_1 14 | - ncurses=6.4=h6a678d5_0 15 | - openssl=3.0.13=h7f8727e_0 16 | - pip=23.3.1=py38h06a4308_0 17 | - python=3.8.18=h955ad1f_0 18 | - readline=8.2=h5eee18b_0 19 | - setuptools=68.2.2=py38h06a4308_0 20 | - sqlite=3.41.2=h5eee18b_0 21 | - tk=8.6.12=h1ccaba5_0 22 | - wheel=0.41.2=py38h06a4308_0 23 | - xz=5.4.5=h5eee18b_0 24 | - zlib=1.2.13=h5eee18b_0 25 | - pip: 26 | - aiosignal==1.3.1 27 | - appdirs==1.4.4 28 | - argparse==1.4.0 29 | - attrs==23.2.0 30 | - automat==22.10.0 31 | - buildtools==1.0.6 32 | - causal-conv1d==1.1.3.post1 33 | - charset-normalizer==3.3.2 34 | - click==8.1.7 35 | - colorama==0.4.4 36 | - colorlog==4.7.2 37 | - constantly==23.10.4 38 | - docker-pycreds==0.4.0 39 | - docopt==0.6.2 40 | - einops==0.7.0 41 | - filelock==3.9.0 42 | - frozenlist==1.4.1 43 | - fsspec==2024.2.0 44 | - furl==2.1.3 45 | - gitdb==4.0.11 46 | - gitpython==3.1.42 47 | - greenlet==3.0.3 48 | - huggingface-hub==0.20.3 49 | - hyperlink==21.0.0 50 | - idna==3.6 51 | - importlib-resources==6.1.2 52 | - incremental==22.10.0 53 | - jinja2==3.1.2 54 | - joblib==1.3.2 55 | - jsonschema==4.21.1 56 | - jsonschema-specifications==2023.12.1 57 | - kmeans-pytorch==0.3 58 | - lightgbm==4.3.0 59 | - mamba-ssm==1.1.4 60 | - mpmath==1.3.0 61 | - msgpack==1.0.7 62 | - networkx==3.0 63 | - ninja==1.11.1.1 64 | - nvidia-cublas-cu11==11.11.3.6 65 | - nvidia-cuda-cupti-cu11==11.8.87 66 | - nvidia-cuda-nvrtc-cu11==11.8.89 67 | - nvidia-cuda-runtime-cu11==11.8.89 68 | - nvidia-cudnn-cu11==8.7.0.84 69 | - nvidia-cufft-cu11==10.9.0.58 70 | - nvidia-curand-cu11==10.3.0.86 71 | - nvidia-cusolver-cu11==11.4.1.48 72 | - nvidia-cusparse-cu11==11.7.5.86 73 | - nvidia-nccl-cu11==2.19.3 74 | - nvidia-nvtx-cu11==11.8.86 75 | - oauthlib==3.2.2 76 | - orderedmultidict==1.0.1 77 | - packaging==23.2 78 | - pillow==10.2.0 79 | - pkgutil-resolve-name==1.3.10 80 | - plotly==5.19.0 81 | - psutil==5.9.8 82 | - pyarrow==15.0.0 83 | - pyyaml==6.0.1 84 | - ray==2.6.3 85 | - recbole==1.2.0 86 | - redo==2.0.4 87 | - referencing==0.33.0 88 | - regex==2023.12.25 89 | - requests==2.31.0 90 | - rpds-py==0.18.0 91 | - safetensors==0.4.2 92 | - scikit-learn==1.3.2 93 | - scipy==1.10.1 94 | - sentry-sdk==1.40.6 95 | - setproctitle==1.3.3 96 | - simplejson==3.19.2 97 | - six==1.16.0 98 | - smmap==5.0.1 99 | - sqlalchemy==2.0.27 100 | - sympy==1.12 101 | - tabulate==0.9.0 102 | - tenacity==8.2.3 103 | - texttable==1.7.0 104 | - thop==0.1.1-2209072238 105 | - threadpoolctl==3.3.0 106 | - tokenizers==0.15.2 107 | # - torch==2.2.1+cu118 108 | # - torchaudio==2.2.1+cu118 109 | # - torchvision==0.17.1+cu118 110 | - tqdm==4.66.2 111 | - transformers==4.38.1 112 | - triton==2.2.0 113 | - twisted==23.10.0 114 | - typing-extensions==4.8.0 115 | - urllib3==1.26.18 116 | - wandb==0.16.3 117 | - xgboost==2.0.3 118 | - zipp==3.17.0 119 | - zope-interface==6.2 120 | prefix: /home/grads/l/liuchengkai/miniconda3/envs/mamba4rec 121 | -------------------------------------------------------------------------------- /mamba4rec.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch import nn 3 | from mamba_ssm import Mamba 4 | from recbole.model.abstract_recommender import SequentialRecommender 5 | from recbole.model.loss import BPRLoss 6 | 7 | class Mamba4Rec(SequentialRecommender): 8 | def __init__(self, config, dataset): 9 | super(Mamba4Rec, self).__init__(config, dataset) 10 | 11 | self.hidden_size = config["hidden_size"] 12 | self.loss_type = config["loss_type"] 13 | self.num_layers = config["num_layers"] 14 | self.dropout_prob = config["dropout_prob"] 15 | 16 | # Hyperparameters for Mamba block 17 | self.d_state = config["d_state"] 18 | self.d_conv = config["d_conv"] 19 | self.expand = config["expand"] 20 | 21 | self.item_embedding = nn.Embedding( 22 | self.n_items, self.hidden_size, padding_idx=0 23 | ) 24 | 25 | self.LayerNorm = nn.LayerNorm(self.hidden_size, eps=1e-12) 26 | self.dropout = nn.Dropout(self.dropout_prob) 27 | 28 | self.mamba_layers = nn.ModuleList([ 29 | MambaLayer( 30 | d_model=self.hidden_size, 31 | d_state=self.d_state, 32 | d_conv=self.d_conv, 33 | expand=self.expand, 34 | dropout=self.dropout_prob, 35 | num_layers=self.num_layers, 36 | ) for _ in range(self.num_layers) 37 | ]) 38 | 39 | if self.loss_type == "BPR": 40 | self.loss_fct = BPRLoss() 41 | elif self.loss_type == "CE": 42 | self.loss_fct = nn.CrossEntropyLoss() 43 | else: 44 | raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!") 45 | 46 | self.apply(self._init_weights) 47 | 48 | def _init_weights(self, module): 49 | if isinstance(module, (nn.Linear, nn.Embedding)): 50 | module.weight.data.normal_(mean=0.0, std=0.02) 51 | elif isinstance(module, nn.LayerNorm): 52 | module.bias.data.zero_() 53 | module.weight.data.fill_(1.0) 54 | if isinstance(module, nn.Linear) and module.bias is not None: 55 | module.bias.data.zero_() 56 | 57 | def forward(self, item_seq, item_seq_len): 58 | item_emb = self.item_embedding(item_seq) 59 | item_emb = self.dropout(item_emb) 60 | item_emb = self.LayerNorm(item_emb) 61 | 62 | for i in range(self.num_layers): 63 | item_emb = self.mamba_layers[i](item_emb) 64 | 65 | seq_output = self.gather_indexes(item_emb, item_seq_len - 1) 66 | return seq_output 67 | 68 | def calculate_loss(self, interaction): 69 | item_seq = interaction[self.ITEM_SEQ] 70 | item_seq_len = interaction[self.ITEM_SEQ_LEN] 71 | seq_output = self.forward(item_seq, item_seq_len) 72 | pos_items = interaction[self.POS_ITEM_ID] 73 | if self.loss_type == "BPR": 74 | neg_items = interaction[self.NEG_ITEM_ID] 75 | pos_items_emb = self.item_embedding(pos_items) 76 | neg_items_emb = self.item_embedding(neg_items) 77 | pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) # [B] 78 | neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) # [B] 79 | loss = self.loss_fct(pos_score, neg_score) 80 | return loss 81 | else: # self.loss_type = 'CE' 82 | test_item_emb = self.item_embedding.weight 83 | logits = torch.matmul(seq_output, test_item_emb.transpose(0, 1)) 84 | loss = self.loss_fct(logits, pos_items) 85 | return loss 86 | 87 | def predict(self, interaction): 88 | item_seq = interaction[self.ITEM_SEQ] 89 | item_seq_len = interaction[self.ITEM_SEQ_LEN] 90 | test_item = interaction[self.ITEM_ID] 91 | seq_output = self.forward(item_seq, item_seq_len) 92 | test_item_emb = self.item_embedding(test_item) 93 | scores = torch.mul(seq_output, test_item_emb).sum(dim=1) # [B] 94 | return scores 95 | 96 | def full_sort_predict(self, interaction): 97 | item_seq = interaction[self.ITEM_SEQ] 98 | item_seq_len = interaction[self.ITEM_SEQ_LEN] 99 | seq_output = self.forward(item_seq, item_seq_len) 100 | test_items_emb = self.item_embedding.weight 101 | scores = torch.matmul( 102 | seq_output, test_items_emb.transpose(0, 1) 103 | ) # [B, n_items] 104 | return scores 105 | 106 | class MambaLayer(nn.Module): 107 | def __init__(self, d_model, d_state, d_conv, expand, dropout, num_layers): 108 | super().__init__() 109 | self.num_layers = num_layers 110 | self.mamba = Mamba( 111 | # This module uses roughly 3 * expand * d_model^2 parameters 112 | d_model=d_model, 113 | d_state=d_state, 114 | d_conv=d_conv, 115 | expand=expand, 116 | ) 117 | self.dropout = nn.Dropout(dropout) 118 | self.LayerNorm = nn.LayerNorm(d_model, eps=1e-12) 119 | self.ffn = FeedForward(d_model=d_model, inner_size=d_model*4, dropout=dropout) 120 | 121 | def forward(self, input_tensor): 122 | hidden_states = self.mamba(input_tensor) 123 | if self.num_layers == 1: # one Mamba layer without residual connection 124 | hidden_states = self.LayerNorm(self.dropout(hidden_states)) 125 | else: # stacked Mamba layers with residual connections 126 | hidden_states = self.LayerNorm(self.dropout(hidden_states) + input_tensor) 127 | hidden_states = self.ffn(hidden_states) 128 | return hidden_states 129 | 130 | class FeedForward(nn.Module): 131 | def __init__(self, d_model, inner_size, dropout=0.2): 132 | super().__init__() 133 | self.w_1 = nn.Linear(d_model, inner_size) 134 | self.w_2 = nn.Linear(inner_size, d_model) 135 | self.activation = nn.GELU() 136 | self.dropout = nn.Dropout(dropout) 137 | self.LayerNorm = nn.LayerNorm(d_model, eps=1e-12) 138 | 139 | def forward(self, input_tensor): 140 | hidden_states = self.w_1(input_tensor) 141 | hidden_states = self.activation(hidden_states) 142 | hidden_states = self.dropout(hidden_states) 143 | 144 | hidden_states = self.w_2(hidden_states) 145 | hidden_states = self.dropout(hidden_states) 146 | hidden_states = self.LayerNorm(hidden_states + input_tensor) 147 | 148 | return hidden_states -------------------------------------------------------------------------------- /run.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import logging 3 | from logging import getLogger 4 | from recbole.utils import init_logger, init_seed 5 | from recbole.trainer import Trainer 6 | from mamba4rec import Mamba4Rec 7 | from recbole.config import Config 8 | from recbole.data import create_dataset, data_preparation 9 | from recbole.data.transform import construct_transform 10 | from recbole.utils import ( 11 | init_logger, 12 | get_model, 13 | get_trainer, 14 | init_seed, 15 | set_color, 16 | get_flops, 17 | get_environment, 18 | ) 19 | 20 | 21 | if __name__ == '__main__': 22 | 23 | config = Config(model=Mamba4Rec, config_file_list=['config.yaml']) 24 | init_seed(config['seed'], config['reproducibility']) 25 | 26 | # logger initialization 27 | init_logger(config) 28 | logger = getLogger() 29 | logger.info(sys.argv) 30 | logger.info(config) 31 | 32 | # dataset filtering 33 | dataset = create_dataset(config) 34 | logger.info(dataset) 35 | 36 | # dataset splitting 37 | train_data, valid_data, test_data = data_preparation(config, dataset) 38 | 39 | # model loading and initialization 40 | init_seed(config["seed"] + config["local_rank"], config["reproducibility"]) 41 | model = Mamba4Rec(config, train_data.dataset).to(config['device']) 42 | logger.info(model) 43 | 44 | transform = construct_transform(config) 45 | flops = get_flops(model, dataset, config["device"], logger, transform) 46 | logger.info(set_color("FLOPs", "blue") + f": {flops}") 47 | 48 | # trainer loading and initialization 49 | trainer = Trainer(config, model) 50 | 51 | # model training 52 | best_valid_score, best_valid_result = trainer.fit( 53 | train_data, valid_data, show_progress=config["show_progress"] 54 | ) 55 | 56 | # model evaluation 57 | test_result = trainer.evaluate( 58 | test_data, show_progress=config["show_progress"] 59 | ) 60 | 61 | environment_tb = get_environment(config) 62 | logger.info( 63 | "The running environment of this training is as follows:\n" 64 | + environment_tb.draw() 65 | ) 66 | 67 | logger.info(set_color("best valid ", "yellow") + f": {best_valid_result}") 68 | logger.info(set_color("test result", "yellow") + f": {test_result}") --------------------------------------------------------------------------------