Abstract
Modern recommender systems target the satisfaction of the end user through the personalization techniques that collects the history of the user’s navigation. But the sole dependency on user profile based on navigation alone cannot promise the quality of recommendations because of the lack of semantics of various aspect such as demographics of the user, time of usage, concept of need etc in the processing. Though the literature provides many techniques to conceptualize the process makes high computational complexity because of the content data considered as input information. In this paper a hybrid recommender framework is developed that considers Meta data based conceptual semantics and the temporal patterns on top of the history of the usage. This framework also includes an online process that identifies the conceptual drift of the usage dynamically. The experimental results shown the effectiveness of the proposed framework when compared to the existing modern recommenders also indicate that the proposed model can resolve a cold start problem yet accurate suggestions reducing computational complexity.
Highlights
A day, recommender system became an important part in all web services and playing a key role of all ecommerce sites such as flipkart, Amazon, Snapdeal etc
The input source of the these semantics in many recommenders is the content of the web pages treated as domain leads to the high computational complexity of the web service
One of the benchmark dataset used in this analysis is from Last.fm database that has the log of music tracks listened by individual users together with a time stamp
Summary
Recommender system became an important part in all web services and playing a key role of all ecommerce sites such as flipkart, Amazon, Snapdeal etc. Traditional approaches of recommender systems suffer from the issues such as cold start and data sparsity. Cold start problem occurs when trying to suggest the new user who does not have much access log so far. These issues arise with lack of semantics and conceptualization in recommendation process. The incorporation of semantics into the recommender process needs the construction of knowledge from the domain of the corresponding web application. The input source of the these semantics in many recommenders is the content of the web pages treated as domain leads to the high computational complexity of the web service. An alternative is much needed to ensure the quality of recommendations and leads to the light weight application with respect to the complexity
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