10,002 research outputs found
Role of Matrix Factorization Model in Collaborative Filtering Algorithm: A Survey
Recommendation Systems apply Information Retrieval techniques to select the
online information relevant to a given user. Collaborative Filtering is
currently most widely used approach to build Recommendation System. CF
techniques uses the user behavior in form of user item ratings as their
information source for prediction. There are major challenges like sparsity of
rating matrix and growing nature of data which is faced by CF algorithms. These
challenges are been well taken care by Matrix Factorization. In this paper we
attempt to present an overview on the role of different MF model to address the
challenges of CF algorithms, which can be served as a roadmap for research in
this area.Comment: 8 pages, 1 figure in IJAFRC, Vol.1, Issue 12, December 201
Scalable and interpretable product recommendations via overlapping co-clustering
We consider the problem of generating interpretable recommendations by
identifying overlapping co-clusters of clients and products, based only on
positive or implicit feedback. Our approach is applicable on very large
datasets because it exhibits almost linear complexity in the input examples and
the number of co-clusters. We show, both on real industrial data and on
publicly available datasets, that the recommendation accuracy of our algorithm
is competitive to that of state-of-art matrix factorization techniques. In
addition, our technique has the advantage of offering recommendations that are
textually and visually interpretable. Finally, we examine how to implement our
technique efficiently on Graphical Processing Units (GPUs).Comment: In IEEE International Conference on Data Engineering (ICDE) 201
Recommendation System for News Reader
Recommendation Systems help users to find information and make decisions where they lack the required knowledge to judge a particular product. Also, the information dataset available can be huge and recommendation systems help in filtering this data according to users‟ needs. Recommendation systems can be used in various different ways to facilitate its users with effective information sorting. For a person who loves reading, this paper presents the research and implementation of a Recommendation System for a NewsReader Application using Android Platform. The NewsReader Application proactively recommends news articles as per the reading habits of the user, recorded over a period of time and also recommends the currently trending articles. Recommendation systems and their implementations using various algorithms is the primary area of study for this project. This research paper compares and details popular recommendation algorithms viz. Content based recommendation systems, Collaborative recommendation systems etc. Moreover, it also presents a more efficient Hybrid approach that absorbs the best aspects from both the algorithms mentioned above, while trying to eliminate all the potential drawbacks observed
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