Time-Aware Based Recommendation System using Gower's Coefficients: Enhancing Personalized Recommendation Gourav Jain, Tripti Mahara, Anil Kumar, S.C. Sharma Procedia Computer Science, 2024 To give users personalised recommendations, the collaborative filtering technique is widely used. In this technique, similarity computation plays a crucial role. Once the similarities among users or items are determined, the system can predict how a user might rate or interact with items that they have not yet encountered. The traditional approaches or measures mainly considered the user-item historical ratings to compute the similarity, while user preferences may change with time. Considering this, the objective of this research is to create an efficient recommendation system that utilized the temporal data. For this, few time decay functions, i.e., exponential, linear, logistic, and power applied to the ratings to give more weightage to the most recent ratings. Since, Gower’s coefficient is suitable for handling the missing data, it is applied to calculate the similarity and compared its results with popular traditional similarity measures i.e., Cosine and Pearson correlation coefficient. Experimental findings on the ML-100k dataset in terms of Root Mean Squared Error (RMSE) and Mean Average Error (MAE) demonstrate that performance of RS improved when we applied a power function on ratings. With comparison to most recent methods PIP, NHSM, RJaccard etc., proposed approach gives almost 10% better results in MAE and 5% better results in RMSE comparison.
A Cognitive Similarity-Based Measure to Enhance the Performance of Collaborative Filtering-Based Recommendation System Gourav Jain, Tripti Mahara, S. C. Sharma, Arun Kumar Sangaiah IEEE Transactions on Computational Social Systems, 2022 Advances in technology and high Internet penetration are leading to a large number of businesses going online. As a result, there is a substantial increase in the number of customers making online purchases and the number of items available online. However, with so many options available to choose from, users have to face the information overload problem. Several techniques have been developed to handle this, but the performance of the recommendation system (RS) has been recorded unprecedentedly. The collaborative filtering (CF) of RS is the most prevalent technique, which suggests personalized items to users based on their past preferences. The efficacy of this technique mainly depends on the similarity calculation, which the traditional or cognitive approach can ascertain. In the traditional approach, a similarity measure utilizes the user’s ratings on an item to compute the similarity. Most similarity measures in this approach suffer from either data sparsity and/or cold-start problems. To address both of them, a new similarity measure based on the Jaccard and Gower coefficients, the efficient Gowers–Jaccard–Sigmoid Measure (EGJSM), is proposed in this article. It also includes a nonlinear sigmoid function to penalize the bad ratings. The performance of EGJSM is evaluated by conducting experiments on benchmark datasets, and the results depict that the proposed technique outperforms several existing methods. Along with this, a cognitive similarity (CgS) measure has been proposed, which considers cognitive features such as genre and year of release along with rating information, to calculate similarity. The CgS method also outperforms the proposed EGJSM method and produces almost 4% and 1% lower mean absolute error (MAE) and root-mean-squared error (RMSE) values than that.
Performance Evaluation of Time-based Recommendation System in Collaborative Filtering Technique Gourav Jain, Tripti Mahara, S.C. Sharma Procedia Computer Science, 2022 The Collaborative Filtering (CF) technique is the most common neighbourhood-based recommendation strategy, that provides personalized recommendation to a user for the items using a similarity measure. Hence, the selection of the appropriate similarity measure becomes crucial in the CF based recommendation system. The traditional similarity measures merely focus only on the historical ratings provided by the users to compute the similarity, completely ignoring the fact that preferences change over a period of time. Considering this, the paper aims to develop an effective Recommendation System that uses temporal information to capture the changes in the preferences over a period of time. For this, the existing exponential and power time decay functions are integrated with Cosine, Pearson Correlation, and Gower's similarity measures to compute similarity. The similarity is computed at the similarity computation and prediction levels of recommendation processes. Experimental findings in terms of MAE and RMSE on the MovieLens-100k demonstrate that performance of Gower's coefficient is better when applied with the exponential function at the similarity computation level of the recommendation process.
An efficient similarity measure to alleviate the cold-start problem Gourav Jain, Tripti Mahara 2019 15th International Conference on Information Processing Internet of Things Icinpro 2019 Proceedings, 2019 The objective of developing a recommender system is to aid users by recommending products that might be of interest to them. In this, the collaborative filtering technique is one of the widely used methods where similarities are calculated among users/items to provide personalized recommendation. In order to calculate the similarity, various similarity measures are used. Most of these similarity methods do not perform satisfactorily in the presence of cold start users. A user is considered cold start if he/she has rated less than twenty items. In case of such users, the minimum available ratings data has to be utilized to recommend items. To resolve this problem, we propose a new similarity measure based on both City Block (CB) and Jaccard measure (CBJ). The co-rated items are considered by City Block while Jaccard measure considers the common items for similarity computation. Thus, when both of these measures are combined, they consider all the co-rated and common items. The main advantage of using CBJ is the reduced computational complexity involved in finding the similarity as compared to other similarity methods. To validate CBJ, we conduct experiments on Film Trust and MiniFilm data sets. The recommendation results on Film trust data set having 872 cold start users out of1508 users and MiniFilm data set having all the 55 cold start users reveal that the proposed CBJ method outperforms other existing methods.
CRLRM: Category based Recommendation using Linear Regression Model Gourav Jain, Nishchol Mishra, Sanjeev Sharma Proceedings 2013 3rd International Conference on Advances in Computing and Communications Icacc 2013, 2013 A system that suggests list of most popular items to a set of users on the basis of their interest is named as recommendation system. Recommendation system filters the unnecessary information by applying knowledge discovery techniques for online users and has become the most powerful and admired tools in E-Business. ERPM is one of the easiest movie recommendation method, which overcomes the limitations of scalability and sparsity of recommendation system, but it generates predictions on the basis of probability model, which are less accurate and requires more time for calculations. This article presents a novel method named CRLRM (Category based Recommendation using Linear Regression Model) which is based on linear regression model that improves the prediction accuracy and speed up the calculations. Performance of proposed method is evaluated on the basis of MAE (Mean Absolute Error) comparison, and result obtained is far much better than ERPM and shows improvement in 30-40% of user ratings.
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