INTELLIGENT RECOMMENDATION SYSTEM.
- Co-Founder & Head of Analytics, Valiance Solutions.
- Data Scientist, Valiance Solutions.
- Indian Institute of Technology, Kharagpur.
- Abstract
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- Corresponding Author
Searching for articles of interest on publication sites can be difficult and time-consuming. Sometimes it takes lot of efforts to find the most relevant article because of which the reader looses interest completely. Recommender systems help the users find articles of their interest with personalized suggestions. In this paper, Hybrid Recommender System is implemented which is a novel combination of content-based filtering, collaborative filtering, trending article algorithm and user persona and recommend articles considering all the possible factors. User short-term interest is catered by suggesting trending articles while long-term interest is catered by observing what kind of content the user prefers to read and by finding out similar users and recommend what they are reading. The model makes the recommendation based on tags assigned to each article and knowledge of articles read by each user. This model doesn't require ratings of articles by each user as generally users usually don’t rate article after reading them as compared to giving rating to movies after watching. The model built takes into consideration many aspects including the trend emerging at current time as well the interest of the user, the time period, geographical location, browsing history etc. then make recommendations accordingly.
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[Shailendra Singh Kathait, Shubhrita Tiwari and Piyush Kumar Singh. (2017); INTELLIGENT RECOMMENDATION SYSTEM. Int. J. of Adv. Res. 5 (Feb). 1649-1656] (ISSN 2320-5407). www.journalijar.com
Co-founder & Head of Analytics
