Prospective Preference Enhanced Mixed Attentive Model for Session-based Recommendation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866914865399988224 |
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| author | Peng, Bo Tai, Chang-Yu Parthasarathy, Srinivasan Ning, Xia |
| author_facet | Peng, Bo Tai, Chang-Yu Parthasarathy, Srinivasan Ning, Xia |
| contents | Session-based recommendation aims to generate recommendations for the next item of users' interest based on a given session. In this manuscript, we develop prospective preference enhanced mixed attentive model (P2MAM) to generate session-based recommendations using two important factors: temporal patterns and estimates of users' prospective preferences. Unlike existing methods, P2MAM models the temporal patterns using a light-weight while effective position-sensitive attention mechanism. In P2MAM, we also leverage the estimate of users' prospective preferences to signify important items, and generate better recommendations. Our experimental results demonstrate that P2MAM models significantly outperform the state-of-the-art methods in six benchmark datasets, with an improvement as much as 19.2%. In addition, our run-time performance comparison demonstrates that during testing, P2MAM models are much more efficient than the best baseline method, with a significant average speedup of 47.7 folds. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_01875 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Prospective Preference Enhanced Mixed Attentive Model for Session-based Recommendation Peng, Bo Tai, Chang-Yu Parthasarathy, Srinivasan Ning, Xia Information Retrieval Session-based recommendation aims to generate recommendations for the next item of users' interest based on a given session. In this manuscript, we develop prospective preference enhanced mixed attentive model (P2MAM) to generate session-based recommendations using two important factors: temporal patterns and estimates of users' prospective preferences. Unlike existing methods, P2MAM models the temporal patterns using a light-weight while effective position-sensitive attention mechanism. In P2MAM, we also leverage the estimate of users' prospective preferences to signify important items, and generate better recommendations. Our experimental results demonstrate that P2MAM models significantly outperform the state-of-the-art methods in six benchmark datasets, with an improvement as much as 19.2%. In addition, our run-time performance comparison demonstrates that during testing, P2MAM models are much more efficient than the best baseline method, with a significant average speedup of 47.7 folds. |
| title | Prospective Preference Enhanced Mixed Attentive Model for Session-based Recommendation |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2206.01875 |