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A Recommendation System Based on Multiple Databases.

Goyal, Vivek

Abstract Details

2013, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
Recommendation Systems have long been serving the industry of e-commerce with recommendations pertaining to movies, books, travel packages et cetera. A user's activity or past history of purchases is used to generate predictions for that user. Youtube's video recommendation system, Amazon's "You may also like..." and Pandora's music recommendation system are a few very popular examples. Both explicit and implicit feedbacks are being utilized to churn out predictions about the likings of a customer to recommend items. As recommendation systems have evolved, we primarily encounter two types- Content based and Collaborative Filtering based recommendation systems. Content based recommendation systems are designed to recommend items similar to the one a user has liked in the past. Recommendation systems based on collaborative filtering recommend items liked by similar users. Users who have liked similar items are identified and items highly liked by those users are recommended. For both content based and collaborative filtering based recommendation systems to predict a rating, it is essential to establish a similarity between items. We have explored correlation and clustering to establish similarity. It was observed that correlation captured similarity better than done by clustering alone. With an intuition that clustering items into similar groups and then employing correlation to determine similarities could improve predictions, we developed an algorithm which is a combination of clustering and correlation that eventually generates prediction for an item rating. We have experimented with adding contextual information to generate better predictions. Our results suggest that predictions generated by using clustering alone got improved by substituting it with correlation. Further, it was seen that a combination of both improved the predictions over clustering alone but correlation still delivered the best results overall. It was established that bringing in more information may not always help. In this thesis we compare these three algorithms and present our analysis with results.
Raj Bhatnagar, Ph.D. (Committee Chair)
Prabir Bhattacharya, Ph.D. (Committee Member)
Karen Davis, Ph.D. (Committee Member)
73 p.

Recommended Citations

Citations

  • Goyal, V. (2013). A Recommendation System Based on Multiple Databases. [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1368027581

    APA Style (7th edition)

  • Goyal, Vivek. A Recommendation System Based on Multiple Databases. 2013. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1368027581.

    MLA Style (8th edition)

  • Goyal, Vivek. "A Recommendation System Based on Multiple Databases." Master's thesis, University of Cincinnati, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1368027581

    Chicago Manual of Style (17th edition)