Context-aware Social Popularity based Recommender System

Abstract

Contexts and social web information have been recognized to be valuable information for making perfect recommender system. Context-aware recommender systems (CARS) have been implemented in different applications and domains which improve the performance of recommendations. Context-aware approaches have been successfully applied in various domains such as music, movies, mobile recommendations, personalized shopping assistants, conversational and interactional services, social rating services and multimedia. The recommender systems are widely being used for products, content and services recommendations. Successful deployment of recommender system in social web and many commercial website like Amazon.com, flipkart, HomeShop18 and numerous different sectors have already done. The growth of the social web has revolutionized the architecture of sharing and association in the web, making it essential to reiterate recommendation. If recommender systems have established their key role in providing the user access to resources on the web, when sharing resources has turn into social, it is likely for recommendation techniques in the social web should consider social popularity factor and the relationships among users to compute their predictions. In this paper contextual information are being included in social popularity based SVD++ model to improve accuracy and scalability of recommendations.

The 1st International Workshop on Context-Aware Recommendation Systems with Big Data Analytics (CARS-BDA)

“…It will align with the emphasis of WSDM on practical yet principled novel models of search and data mining, algorithm design and analysis, and relevant to the topics of WSDM, including multimodal data mining, web recommender systems and algorithms, social search, mining and other applications, social network dynamics, locationbased social networks, social network analysis, theories, models and applications. e areas of interest mainly include contexts and user behaviors in recommendation, such as behavioural categories [1], article popularity [3,12], social trust [4], user activities and interactions [2,[5][6][7][8][9][10][11], and the scalability of recommender systems such as the Apache Storm-based parallel processing [2,9,13] etc. In particular, topics of interest for this workshop include (but are not limited to):…”