Text Matching Improves Sequential Recommendation by Reducing Popularity Biases
This paper proposes Text mAtching based SequenTial rEcommendation model (TASTE), which maps items and users in an embedding space and recommends items by matching their text representations. TASTE verbalizes items and user-item interactions using identifiers and attributes of items. To better charac...
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creator | Liu, Zhenghao Mei, Sen Xiong, Chenyan Li, Xiaohua Yu, Shi Liu, Zhiyuan Gu, Yu Yu, Ge |
description | This paper proposes Text mAtching based SequenTial rEcommendation model
(TASTE), which maps items and users in an embedding space and recommends items
by matching their text representations. TASTE verbalizes items and user-item
interactions using identifiers and attributes of items. To better characterize
user behaviors, TASTE additionally proposes an attention sparsity method, which
enables TASTE to model longer user-item interactions by reducing the
self-attention computations during encoding. Our experiments show that TASTE
outperforms the state-of-the-art methods on widely used sequential
recommendation datasets. TASTE alleviates the cold start problem by
representing long-tail items using full-text modeling and bringing the benefits
of pretrained language models to recommendation systems. Our further analyses
illustrate that TASTE significantly improves the recommendation accuracy by
reducing the popularity bias of previous item id based recommendation models
and returning more appropriate and text-relevant items to satisfy users. All
codes are available at https://github.com/OpenMatch/TASTE. |
doi_str_mv | 10.48550/arxiv.2308.14029 |
format | Article |
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(TASTE), which maps items and users in an embedding space and recommends items
by matching their text representations. TASTE verbalizes items and user-item
interactions using identifiers and attributes of items. To better characterize
user behaviors, TASTE additionally proposes an attention sparsity method, which
enables TASTE to model longer user-item interactions by reducing the
self-attention computations during encoding. Our experiments show that TASTE
outperforms the state-of-the-art methods on widely used sequential
recommendation datasets. TASTE alleviates the cold start problem by
representing long-tail items using full-text modeling and bringing the benefits
of pretrained language models to recommendation systems. Our further analyses
illustrate that TASTE significantly improves the recommendation accuracy by
reducing the popularity bias of previous item id based recommendation models
and returning more appropriate and text-relevant items to satisfy users. All
codes are available at https://github.com/OpenMatch/TASTE.</description><identifier>DOI: 10.48550/arxiv.2308.14029</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Information Retrieval</subject><creationdate>2023-08</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,781,886</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2308.14029$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2308.14029$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Liu, Zhenghao</creatorcontrib><creatorcontrib>Mei, Sen</creatorcontrib><creatorcontrib>Xiong, Chenyan</creatorcontrib><creatorcontrib>Li, Xiaohua</creatorcontrib><creatorcontrib>Yu, Shi</creatorcontrib><creatorcontrib>Liu, Zhiyuan</creatorcontrib><creatorcontrib>Gu, Yu</creatorcontrib><creatorcontrib>Yu, Ge</creatorcontrib><title>Text Matching Improves Sequential Recommendation by Reducing Popularity Biases</title><description>This paper proposes Text mAtching based SequenTial rEcommendation model
(TASTE), which maps items and users in an embedding space and recommends items
by matching their text representations. TASTE verbalizes items and user-item
interactions using identifiers and attributes of items. To better characterize
user behaviors, TASTE additionally proposes an attention sparsity method, which
enables TASTE to model longer user-item interactions by reducing the
self-attention computations during encoding. Our experiments show that TASTE
outperforms the state-of-the-art methods on widely used sequential
recommendation datasets. TASTE alleviates the cold start problem by
representing long-tail items using full-text modeling and bringing the benefits
of pretrained language models to recommendation systems. Our further analyses
illustrate that TASTE significantly improves the recommendation accuracy by
reducing the popularity bias of previous item id based recommendation models
and returning more appropriate and text-relevant items to satisfy users. All
codes are available at https://github.com/OpenMatch/TASTE.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Information Retrieval</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz8tOwzAUBFBvWKDCB7DCP5Dg-MaxvYSKR6XyEGQfXbs3YCkvEqdq_h5aWI00Go10GLvKRJobpcQNjoewTyUIk2a5kPacvZR0iPwZo_8K3SfftMPY72niH_Q9UxcDNvydfN-21O0whr7jbvltdrM_zt_6YW5wDHHhdwEnmi7YWY3NRJf_uWLlw325fkq2r4-b9e02wULbBJy0CjKNXskcrPAKpTG5LjKnHNpCK3IkAL1D0ADobO2UNjUZLz0VBCt2_Xd7AlXDGFocl-oIq04w-AHo0UmZ</recordid><startdate>20230827</startdate><enddate>20230827</enddate><creator>Liu, Zhenghao</creator><creator>Mei, Sen</creator><creator>Xiong, Chenyan</creator><creator>Li, Xiaohua</creator><creator>Yu, Shi</creator><creator>Liu, Zhiyuan</creator><creator>Gu, Yu</creator><creator>Yu, Ge</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20230827</creationdate><title>Text Matching Improves Sequential Recommendation by Reducing Popularity Biases</title><author>Liu, Zhenghao ; Mei, Sen ; Xiong, Chenyan ; Li, Xiaohua ; Yu, Shi ; Liu, Zhiyuan ; Gu, Yu ; Yu, Ge</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a679-3b295317ac524390c5a2884761b5ba9675ebe03acba3733ab9fb578fe8c2ce6e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Information Retrieval</topic><toplevel>online_resources</toplevel><creatorcontrib>Liu, Zhenghao</creatorcontrib><creatorcontrib>Mei, Sen</creatorcontrib><creatorcontrib>Xiong, Chenyan</creatorcontrib><creatorcontrib>Li, Xiaohua</creatorcontrib><creatorcontrib>Yu, Shi</creatorcontrib><creatorcontrib>Liu, Zhiyuan</creatorcontrib><creatorcontrib>Gu, Yu</creatorcontrib><creatorcontrib>Yu, Ge</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Liu, Zhenghao</au><au>Mei, Sen</au><au>Xiong, Chenyan</au><au>Li, Xiaohua</au><au>Yu, Shi</au><au>Liu, Zhiyuan</au><au>Gu, Yu</au><au>Yu, Ge</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Text Matching Improves Sequential Recommendation by Reducing Popularity Biases</atitle><date>2023-08-27</date><risdate>2023</risdate><abstract>This paper proposes Text mAtching based SequenTial rEcommendation model
(TASTE), which maps items and users in an embedding space and recommends items
by matching their text representations. TASTE verbalizes items and user-item
interactions using identifiers and attributes of items. To better characterize
user behaviors, TASTE additionally proposes an attention sparsity method, which
enables TASTE to model longer user-item interactions by reducing the
self-attention computations during encoding. Our experiments show that TASTE
outperforms the state-of-the-art methods on widely used sequential
recommendation datasets. TASTE alleviates the cold start problem by
representing long-tail items using full-text modeling and bringing the benefits
of pretrained language models to recommendation systems. Our further analyses
illustrate that TASTE significantly improves the recommendation accuracy by
reducing the popularity bias of previous item id based recommendation models
and returning more appropriate and text-relevant items to satisfy users. All
codes are available at https://github.com/OpenMatch/TASTE.</abstract><doi>10.48550/arxiv.2308.14029</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Artificial Intelligence Computer Science - Information Retrieval |
title | Text Matching Improves Sequential Recommendation by Reducing Popularity Biases |
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