Timeliness in recommender systems |
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| Affiliation: | 1. School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, 330013, P.R. China;2. School of Systems Science, Beijing Normal University, Beijing, 100875, P. R. China;1. Department of Computer Engineering, Federal University of Technology - Parana, UTFPR, Toledo Campus, Toledo, Parana, Brazil;2. Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany;3. Industrial and Systems Engineering Graduate Program, LAS/PPGEPS Pontifical Catholic University of Parana, PUCPR, Imaculada Conceição, 1155, Zip code 80215-901, Curitiba, Parana, Brazil;4. Department of Electrical Engineering, Federal University of Parana, UFPR C. P. 19011, Polytechnic Center, Zip code 81531-970, Curitiba, Parana, Brazil;1. University of Nova Gorica, Nova Gorica, Slovenia;2. Jo?ef Stefan Institute, Ljubljana, Slovenia;3. Temida d.o.o., Ljubljana, Slovenia;1. Department of Engineering and Technology, Texas A&M University - Commerce, 2200 Campbell St, Commerce, TX 75429-3011, USA;2. Department of Systems and Industrial Engineering, University of Arizona, 1127 E. James E. Rogers Way, Room 111, Tucson, AZ 85721-0020, USA;3. Metropia, Inc., 3701 Executive Center Dr. STE 209, Austin, TX 78750, USA;4. Department of Civil Engineering and Engineering Mechanics, The University of Arizona, 1209 E. Second St., Room 206A, Tucson, AZ, USA;5. AAA Foundation for Traffic Safety, 601 14th Street, NW, Suite 201, Washington, DC 2005-2000, USA;1. COPPEAD Graduate Business School, Federal University of Rio de Janeiro, Rua Paschoal Lemme, 355. 21949-900 Rio de Janeiro, Brazil;2. Crummer Graduate School of Business, Rollins College, 1000 Holt Ave. – 2722, Winter Park, Fl 32789, USA;3. Department of Mathematics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran;4. Department of Business Administration, School of Business, Bangladesh Army International University of Science and Technology, Comilla-3501, Bangladesh;1. Faculty of Information Technology, University of Science, VNU-HCM, Viet Nam;2. Faculty of Information Technology, Ho Chi Minh City University of Technology, Viet Nam;3. College of Electronics and Information Engineering, Sejong University, Seoul, Republic of Korea;4. Division of Data Science, Ton Duc Thang University, Ho Chi Minh City, Viet Nam;5. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Viet Nam;6. Key Laboratory of Machine Perception (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China |
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| Abstract: | Due to the high efficiency in finding the most relevant online products for users from the information ocean, recommender systems have now been applied to many commercial web sites. Meanwhile, many recommendation algorithms have been developed to improve the recommendation accuracy and diversity. However, whether the recommended items are timely or not in these algorithms has not yet been well understood. To investigate this problem, we consider a temporal data division which divides the links to probe set and training set strictly according to the time stamp on links. We find that the recommendation accuracy of many algorithms are much lower in temporal data division than in the random data division.With a timeliness metric, we find that the low accuracy is caused by the tendency of these algorithms to recommend out-of-date items, which cannot be detected with the random data division. To solve this problem, we improve the considered recommendation algorithms with a timeliness factor. The resulting algorithms can strongly suppress the probability of recommending obsolete items. Meanwhile, the recommendation accuracy is substantially enhanced. |
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