HCF-CRS: A Hybrid Content based Fuzzy Conformal Recommender System for providing recommendations with confidence

A Recommender System (RS) is an intelligent system that assists users in finding the items of their interest (e.g. books, movies, music) by preventing them to go through huge piles of data available online. In an effort to overcome the data sparsity issue in recommender systems, this research incorporates a content based filtering technique with fuzzy inference system and a conformal prediction approach introducing a new framework called Hybrid Content based Fuzzy Conformal Recommender System (HCF-CRS). The proposed framework is implemented to be used in the domain of movies and it provides quality recommendations to users with a confidence level and an improved accuracy. In our proposed framework, first, a Content Based Filtering (CBF) technique is applied to create a user profile by considering the history of each user. CBF is useful in the situations like: lack of demographic information and the data sparsity problems. Second, a Fuzzy based technique is incorporated to find the similarities and differences between the user profile and the movies in the dataset using a set of fuzzy rules to get a predicted rating for each movie. Third, a Conformal prediction algorithm is implemented to calculate the non-conformity measure between the predicted ratings produced by fuzzy system and the actual ratings from the dataset. A p-value (confidence measure) is computed to give a level of confidence to each recommended item and a bound is set on the confidence level called a significance level ε, according to which the movies only above the specified significance level are recommended to user. By building a confidence centric hybrid conformal recommender system using the content based filtering approach with fuzzy logic and conformal prediction algorithm, the reliability and the accuracy of the system is considerably enhanced. The experiments are evaluated on MovieLens and Movie Tweetings datasets for recommending movies to the users and they are compared with other state-of-the-art recommender systems. Finally, the results confirm that the proposed algorithms perform better than the traditional ones

DeepRank: Adapting Neural Tensor Networks for Ranking the Recommendations

Online real estate property portals are gaining great attraction from masses due to ease in finding properties for rental or sale/purchase. With a few clicks, a real estate portal can display relevant information to a user by ranking the searched items according to user’s specifications. It is highly significant that the ranking results display the most relevant search results to the user. Therefore, an efficient ranking algorithm that takes user’s context is crucial for enhancing user experience in finding real estate properties online. This paper proposes an expressive Neural Tensor Network to rank the properties when searched for based on the similarity between the two property entities. Previous similarity techniques do not take into account the numerous complex features used to define a property. We showed that the performance can be enhanced if the property entities are represented as an average of their constituting features before finding the similarity between them. The proposed method takes into account each feature dynamically and ranks properties according to similarity with an accuracy of 86.6%