Sharu Vinayak ¹, Richa Sharma ¹, Rahul Singh ¹

Affiliations
1 Department of Computer Science and Engineering, Chandigarh University, IN

Abstract

Objective: We propose a novel idea for resolving research issues like cross domain recommendations and recommendations using social networks in the emerging research field recommender systems. Methods/Analysis: According to this idea user will be recommended with the list of books that belong to the genre that is most liked by the user in terms of movies. Findings: Here we will collect user's tastes in movies from his social network profile and extract out the most liked genre by him and using an appropriate collaborative filtering algorithm will recommend him with the books that may interest him. Improvement: The proposed idea is expected to resolve research problems like cold start problem and sparsity. Our proposed methodology gives more competent results than the traditional.

Keywords

Cross Domain Recommendations, F1 Score, Precision, Recall, Recommender Systems, Root Mean Square Error.

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Ravishanker ¹, Ashish Kr. Luhach ¹, Richa Sharma ¹

Affiliations
1 Lovely Professional University, Phagwara - 144411, Punjab, IN

Abstract

Background/Objectives: The community overlapping is the process by which number of nodes within the mesh network share common resources. The shared resources could lead to the conflict such as inconsistent analysis problem. Study of these problems is the objective of the paper. Methods/Statistical Analysis: In order to analyze the problem Modified K-Clique with sink node elimination technique is suggested. K-Clique method used detects the nodes in the mesh network having more than one connection. The modification to K-Clique enhance speed since sink node is eliminated prior to calculation of cliques. The adjacency matrix is used in order to detect the sink nodes. The Simulation is conducted in MATLAB. The MATLAB provides tools of network programming in terms of plots and graphs. The existing K-Clique is compared against the modified K-Clique and result obtained is better for Modified k-Clique. Findings: The speed is enhanced almost by 5% and number of cliques of distinct sizes discovered is also increased by 5%. The speed can further be enhanced by following hop count mechanism to reach destination quickly in addition to sink node elimination. Application/Improvement: Enhancement of performance using community overlapping detection in wireless mesh network through which it is possible to transfer the data towards multiple destinations with the help of community overlapping detection. Multiple destination towards which is to be transferred can be detected. Time will be less consumed in this case. The distance vector routing can be merged in the supposed system to further enhance the scope of the system.

Keywords

Community Overlapping, Complex Network, Distance Vector, K-Clique, Sink Nodes.

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Richa Sharma ¹, Rahul Singh ¹

Affiliations
1 University Institute of Engineering, Chandigarh University, Gharuan, Mohali - 140413, Punjab, IN

Abstract

Recommender systems were introduced in the mid-1990s to help people select the most suitable product for them from the plethora of options available with them. The idea that led to their development was that we people often rely on the opinions of our peers before trying something new, say it be before buying a smart phone, a laptop, before going for a movie, before going to a new restaurant and even before visiting a doctor. Till date, we have numerous recommender systems developed for various areas, using different recommendation approaches. Yet, there are still a few limitations of recommender systems that need to be worked on. In this paper, we present an overview of recommender systems, the various approaches of recommender systems, the application areas for which various recommender systems have been developed and we also present the limitations of recommender systems.

Keywords

Application Ares, Limitations, Metrics, Recommender Systems, Recommendation Appaches, Recommendation Algorithms.

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