Faculty Publications
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Publications by NITK Faculty
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Item An improved web page recommendation system using partitioning and web usage mining(Association for Computing Machinery acmhelp@acm.org, 2015) Chanda, J.; Annappa, B.There are different types of hypertext documents available on the Internet. Accessing relevant information and serving useful information to the user from the Internet has become a complex and expensive task. To make this process simpler, one of the widely used recommendation systems is item based collaborative filtering recommendation system which predicts web pages based on the browsing activity of the user on the Internet and recommends web pages as per their interests. There are certain challenges in these systems like sparsity and scalability, the proposed approach overcomes these problems. The proposed approach uses weighted kmean clustering instead of simple k-mean clustering and the obtained clusters are partitioned on the basis of similarity which helps in reducing the processing time of recommendation generation. Clustering and partitioning enhances the existing item based collaborative filtering recommendation system. The MovieLens data set is used for demonstrating the proposed approach. The performance of the proposed approach is evaluated using various metrics. The result shows that the proposed approach is 30% efficient in terms of root mean square error and 21% effective in respect of mean absolute error analysis and the accuracy measures factors like precision, recall and F-measure are found to have higher values than the existing item based collaborative filtering recommendation systems. © 2015 ACM.Item Travel Recommendation System Using Geotagged Photos(Association for Computing Machinery acmhelp@acm.org, 2017) Kumari, A.; Singh, A.K.; Patil, N.Recently in multimedia, web services contain a huge volume of geo-tagged photos. The users who upload these photos are sharing their travel experiences through them. Geo-tagged photos have crucial information imbibed within them, like location, time, tags and weather. Travel Recommendation methods that exist do not take into consideration user preferences and weather all at once. In this paper, a travel recommendation system is proposed for tourists in Mumbai according to their preferences, weather and live events. The preferences are obtained according to the prior travel history of user(s) and recommendations are suggested. Dataset is collected from the Flickr API and the technique is examined for Mumbai, an Indian metropolitan city. The effectiveness of the proposed method can be seen from the experimental results, which shows an average of 15% improvement in the accuracy with respect to the existing methods. © 2017 Association for Computer Machinery.Item Friendship recommendation system using topological structure of social networks(Springer Verlag service@springer.de, 2018) Kumar, P.; Guddeti, G.Popularity and importance of Recommendation System is being increased day by day in both commercial and research community. Social networks (SNs) like Facebook, Twitter, and LinkedIn draw more attention since without any previous knowledge a lot of connections have been established. The creation of relationship between users is the key feature of a social network. Therefore, it is important for researchers to look for a new way to provide recommendations with more relevance. This paper proposes two algorithms for recommending a new friend in online social networks. The first algorithm is based on the number of mutual friends and second is based on influence score. These recommendation algorithms use collaborative filtering and provide the idea of doing recommendations (e.g., Facebook recommend friends, Netflix suggest movies, Amazon recommend products, etc.). Obtained results and analysis indicate that influence-based recommendation system is more accurate as compared to mutual friend-based recommendation. These proposed recommendation algorithms can be used for the development of an effective social networking or e-commerce site and thereby providing a better experience to users. © Springer Nature Singapore Pte Ltd. 2018.Item A Hybrid Approach to Predict Ratings for Book Recommendation System Using Machine Learning Techniques(Institute of Electrical and Electronics Engineers Inc., 2024) Roy, T.; Shetty D, P.A recommender system is a tool that suggests products or services to users based on their preferences and past behavior, enhancing user satisfaction and engagement. Accurate rating prediction is crucial as it directly impacts the system's ability to provide relevant and personalized recommendations, thereby improving the overall user experience. In this study, we introduce an innovative approach to recommendation systems by proposing an Weighted Hybrid Model that combines an Adaptive K-Nearest Neighbors (AKNN) algorithm and Singular Value Decomposition (SVD). The AKNN algorithm dynamically adjusts the number of neighbors based on user rating density, providing a tailored neighborhood size for each user. By incorporating a hybrid similarity measure that combines cosine similarity, Pearson correlation, and Variance Mean Difference (VMD), our AKNN algorithm effectively captures the multifaceted nature of user-item relationships. We further enhance our recommendation model by combining AKNN with SVD through optimized weighting, creating a Weighted Hybrid Model. This model balances the contributions of the AKNN and SVD components, leveraging the strengths of both approaches to minimize prediction errors. Our evaluation results demonstrate that the Weighted Hybrid Model outperforms several algorithms, including standalone KNN with Z-Score, Item-wise Variance-Mean based Recommender System (IVMRS), KNN with RJAC DUB, and Pearson Baseline with Weighted KNN. The Weighted Hybrid Model achieved the lowest Root Mean Squared Error (RMSE) of 1.54491 and Mean Absolute Error (MAE) of 1.17839, indicating superior predictive accuracy. © 2024 IEEE.