{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T10:45:35Z","timestamp":1771065935945,"version":"3.50.1"},"reference-count":24,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,11,29]],"date-time":"2018-11-29T00:00:00Z","timestamp":1543449600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>A recommendation system can recommend items of interest to users. However, due to the scarcity of user rating data and the similarity of single ratings, the accuracy of traditional collaborative filtering algorithms (CF) is limited. Compared with user rating data, the user\u2019s behavior log is easier to obtain and contains a large amount of implicit feedback information, such as the purchase behavior, comparison behavior, and sequences of items (item-sequences). In this paper, we proposed a personalized recommendation algorithm based on a user\u2019s implicit feedback (BUIF). BUIF considers not only the user\u2019s purchase behavior but also the user\u2019s comparison behavior and item-sequences. We extracted the purchase behavior, comparison behavior, and item-sequences from the user\u2019s behavior log; calculated the user\u2019s similarity by purchase behavior and comparison behavior; and extended word-embedding to item-embedding to obtain the item\u2019s similarity. Based on the above method, we built a secondary reordering model to generate the recommendation results for users. The results of the experiment on the JData dataset show that our algorithm shows better improvement in regard to recommendation accuracy over other CF algorithms.<\/jats:p>","DOI":"10.3390\/fi10120117","type":"journal-article","created":{"date-parts":[[2018,11,29]],"date-time":"2018-11-29T11:47:53Z","timestamp":1543492073000},"page":"117","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A Personalized Recommendation Algorithm Based on the User\u2019s Implicit Feedback in E-Commerce"],"prefix":"10.3390","volume":"10","author":[{"given":"Bo","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feiyue","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jialu","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1023\/A:1021240730564","article-title":"Hybrid recommender systems: Survey and experiments","volume":"12","author":"Burke","year":"2002","journal-title":"User Model. 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