Recommender System using Collaborative Filtering and Demographic Characteristics of Users

Recommender systems use variety of data mining techniques and algorithms to identify relevant preferences of items for users in a system out of available millions of choices. Recommender systems are classified into Collaborative filtering, Content-Based filtering, Knowledge-Based filtering and Hybrid filtering systems. The traditional recommender systems approaches are facing many challenges like data sparsity, cold start problem, scalability, synonymy, shilling attacks, gray sheep and black sheep problems. These problems consequently degrade the performance of recommender systems to a great extent. Among these cold start problem is one of the challenges which comes into scene when either a new user enters into a system or a new product arrives in catalogue. Both situations lead to difficulty in predicting user preferences due to non-availability of sufficient user rating history. The study proposes a new hybrid recommender system framework for solving new user cold-start problem by exploiting user demographic characteristics for finding similarity between new user and already existing users in the system. The efficiency of recommender systems can be improved by proposed approach which calculates recommendations for new user by predicting preferences within much smaller cluster rather than from the entire customer base. The analysis has been done using MovieLens dataset for enhancing the performance of online movie recommendation system. DOI: 10.17762/ijritcc2321-8169.15077

Evaluating collaborative filtering over time

Recommender systems have become essential tools for users to navigate the plethora of content in the online world. Collaborative filtering—a broad term referring to the use of a variety, or combination, of machine learning algorithms operating on user ratings—lies at the heart of recommender systems’ success. These algorithms have been traditionally studied from the point of view of how well they can predict users’ ratings and how precisely they rank content; state of the art approaches are continuously improved in these respects. However, a rift has grown between how filtering algorithms are investigated and how they will operate when deployed in real systems. Deployed systems will continuously be queried for personalised recommendations; in practice, this implies that system administrators will iteratively retrain their algorithms in order to include the latest ratings. Collaborative filtering research does not take this into account: algorithms are improved and compared to each other from a static viewpoint, while they will be ultimately deployed in a dynamic setting. Given this scenario, two new problems emerge: current filtering algorithms are neither (a) designed nor (b) evaluated as algorithms that must account for time. This thesis addresses the divergence between research and practice by examining how collaborative filtering algorithms behave over time. Our contributions include: 1. A fine grained analysis of temporal changes in rating data and user/item similarity graphs that clearly demonstrates how recommender system data is dynamic and constantly changing. 2. A novel methodology and time-based metrics for evaluating collaborative filtering over time, both in terms of accuracy and the diversity of top-N recommendations. 3. A set of hybrid algorithms that improve collaborative filtering in a range of different scenarios. These include temporal-switching algorithms that aim to promote either accuracy or diversity; parameter update methods to improve temporal accuracy; and re-ranking a subset of users’ recommendations in order to increase diversity. 4. A set of temporal monitors that secure collaborative filtering from a wide range of different temporal attacks by flagging anomalous rating patterns. We have implemented and extensively evaluated the above using large-scale sets of user ratings; we further discuss how this novel methodology provides insight into dimensions of recommender systems that were previously unexplored. We conclude that investigating collaborative filtering from a temporal perspective is not only more suitable to the context in which recommender systems are deployed, but also opens a number of future research opportunities

InnoJam: A Web 2.0 discussion platform featuring a recommender system

In this Master Thesis we have designed, implemented and evaluated a Web 2.0 platform for massive online-discussion, inspired by Innovation Jams. Innovation Jams, the original initiative from IBM, has proven to be successful at bringing together vast amounts of people, capturing their untapped knowledge and, while the participants are discussing, gather useful insights for a companyʼs innovation strategy [Spangler et al. 2006, Bjelland and Chapman Wood 2008]. Our approach, based in an open-source forum system, features visualization techniques and a recommender system in order to provide the participants in the Jam with useful insights and interesting discussion recommendations for an improved participation. A theoretical introduction and a state-of-the-art survey in recommender systems has been gathered in order to frame and support the design of the hybrid recommender system [Burke 2002], composed by a content-based and a collaborative filtering recommenders, developed for InnoJam

A Hybrid Web Recommendation System based on the Improved Association Rule Mining Algorithm

As the growing interest of web recommendation systems those are applied to deliver customized data for their users, we started working on this system. Generally the recommendation systems are divided into two major categories such as collaborative recommendation system and content based recommendation system. In case of collaborative recommen-dation systems, these try to seek out users who share same tastes that of given user as well as recommends the websites according to the liking given user. Whereas the content based recommendation systems tries to recommend web sites similar to those web sites the user has liked. In the recent research we found that the efficient technique based on asso-ciation rule mining algorithm is proposed in order to solve the problem of web page recommendation. Major problem of the same is that the web pages are given equal importance. Here the importance of pages changes according to the fre-quency of visiting the web page as well as amount of time user spends on that page. Also recommendation of newly added web pages or the pages those are not yet visited by users are not included in the recommendation set. To over-come this problem, we have used the web usage log in the adaptive association rule based web mining where the asso-ciation rules were applied to personalization. This algorithm was purely based on the Apriori data mining algorithm in order to generate the association rules. However this method also suffers from some unavoidable drawbacks. In this paper we are presenting and investigating the new approach based on weighted Association Rule Mining Algorithm and text mining. This is improved algorithm which adds semantic knowledge to the results, has more efficiency and hence gives better quality and performances as compared to existing approaches.Comment: 9 pages, 7 figures, 2 table

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201