Handling cold start problem in Recommender Systems by using Interaction Based Social Proximity factor

摘要

Recommender Systems (RS) help users to find items and make choices which may suit their taste and needs. A user's past behavior, taste and general buying trends may effectively be used by a RS to suggest items to the user each time he/she enters an e-commerce website. However if the user is new and has no awareness of the available choices (cold start user), it becomes somewhat difficult for the system to offer recommendations. This limitation, known as the cold start problem, has been one of the mostly explored challenges in the Recommender Systems research. In the current work, an attempt is made to handle cold start problem by generating recommendations based on the social interactions between the users on Facebook, a popular social networking website. The choices made by friends or acquaintances, tend to have an impact on the user's opinions and choices. We incorporate this concept to make recommendations to the user. We propose “Interaction Based Social Proximity (IBSP)”, a social interaction factor to overcome the Cold Start problem. A prototype of the system has been developed for the books domain using java. The Facebook graph API is used to extract information from the social graph of the user.