Abstract
Recommender systems in e-commerce applications have become business relevant in filtering the vast range of information available in web shop (and the internet) to present useful recommendation to user. In this study we combine social network analysis and semantic user profile to provide a new semantic-social recommendation, featuring a two-stage process that relies on a simple formalization of semantic user preferences that contains the user's main interests and heuristically explores the social graph. Given a recommendation request concerning a product, the semantic-social recommendation algorithm compares the user preferences, which are found in the exploration path, with the product preferences by referencing them to domain ontology. Experiments on real-world data from Amazon, examine the quality of our recommendation method as well as the efficiency of our recommendation algorithms.
Highlights
The exponential growth of the vast range of information poses challenges and presents new opportunities for recommender system research
Recommender systems has three main categories (Melville and Sindhwani, 2010): Content-based (Pazzani and Billsus, 2007) where the users are recommended with items that are similar to those that they liked in the past, collaborative-filtering or social recommendation (Das et al, 2007) where the recommendation depends on the user's neighbors' opinions and not on the item itself and hybrid recommendation that combines the content-based and social based recommendation methods (Burke, 2007)
In this study we present a solution to surpass the defects of failure recommendation by presenting semantic-social recommendation algorithm, in which we suppose a set of user and a set of products such as a users are connected through a social network and users and items are described via taxonomy
Summary
The exponential growth of the vast range of information poses challenges and presents new opportunities for recommender system research. In this study we present a solution to surpass the defects of failure recommendation by presenting semantic-social recommendation algorithm, in which we suppose a set of user and a set of products such as a users are connected through a social network and users and items are described via taxonomy In this setting, given a product we use a heuristic based search algorithm to search the social network in order to compute a relevant set of users to whom the product can be recommended. Given a product we use a heuristic based search algorithm to search the social network in order to compute a relevant set of users to whom the product can be recommended This algorithm is concerned with two important aspects, the social aspect by using social network analysis measures and the semantic aspect by using the semantic similarity measures
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