Sequencing of items in personalized recommendations using multiple recommendation techniques

•Proposed approach has high precision value for small top-n recommendations.•Sequencing of items in recommendation list is made on basis of popularity.•Both ratings and opinions of users are used about items.•Handles item side cold start and gray sheep problems in fairly simple manner.•Experiment shows around 20% improvement in precision for small n values. Recommendation System (RS) is a piece of software that gives suggestions according to the interest of users in many domains like products in e-commerce, tours, hotels, entertainment etc. In any of the established e-commerce website hundreds of products are available under the same category. RS helps buyers to find the right product based on buyer’s past buying pattern and item information. Currently many established approaches for item recommendations like content based filtering, collaborative filtering, matrix factorization, etc., exist. All these approaches create a big list of item recommendations for the target user. In general most users prefer to see only top-n recommendations, where the value of n is small and just ignores remaining recommendations. It means good RS must have high precision value for smaller values of n but at present almost all recommendation systems to the best of authors’ knowledge are having high recall value and low precision value. It clearly means that top-n recommendations generated by these systems have very few items that may be liked by the target user. The proposed approach generates recommendations by combining features of content based filtering, collaborative filtering, matrix factorization and opinion mining. The proposed RS dynamically keeps track of user’s inclination towards different types of items with respect to time. It analyzes user’s opinions about products and finds the product popularity in the market by its own unique way. In the proposed approach, items are arranged in such a way that almost all preferred items by target user comes under top-n recommendations. The experimental results show that top-n recommendations generated by the proposed approach for smaller value of n have high precision value when compared with other traditional benchmark recommendation methods.