Combined Recommendation Algorithm Based on Improved Similarity and Forgetting Curve
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The recommendation algorithm in e-commerce systems is faced with the problem of high sparsity of users’ score data and interest’s shift, which greatly affects the performance of recommendation. Hence, a combined recommendation algorithm based on improved similarity and forgetting curve is proposed. Firstly, the Pearson similarity is improved by a wide range of weighted factors to enhance the quality of Pearson similarity for high sparse data. Secondly, the Ebbinghaus forgetting curve is introduced to track a user’s interest shift. User score is weighted according to the residual memory of forgetting function. Users’ interest changing with time is tracked by scoring, which increases both accuracy of recommendation algorithm and users’ satisfaction. The two algorithms are then combined together. Finally, the MovieLens dataset is employed to evaluate different algorithms and results show that the proposed algorithm decreases mean absolute error (MAE) by 12.2%, average coverage 1.41%, and increases average precision by 10.52%, respectively.Keywords:
MovieLens
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Collaborative filtering algorithm is one of the most widely used algorithms in recommender systems and has demonstrated promising results. But it relies too much on similarity to find the nearest neighbors. Whatever, the trust between users is also an import factor needed to be considered. This paper proposed a collaborative filtering algorithm that combined the user similarity and trust to obtain a more appropriate nearest neighbors set. Users not only have same interests as their nearest neighbors, but also have higher level of acceptance in the items recom-mended by their nearest neighbors. Extensive experiments based on Film Trust and MovieLens datasets have shown that the approach has major potential in improving the accuracy of recommended item.
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A collaborative filtering hybrid recommendation algorithm based on the idea of optimal combination prediction (BEST-CF) is proposed, and the effectiveness of BEST-CF is verified on Movielens 100K data set using the optimal combination of the user-based collaborative filtering recommendation algorithm (User-CF) and the item-based collaborative filtering recommendation algorithm (Item-CF). Experiments results show that the BEST-CF algorithm significantly improves the rating prediction accuracy and can enhance the recommendation quality.
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The popularity of movies has increased in recent years. There are thousands of films produced each year. These films make it challenging for movie lovers to pick the ideal film to see. We propose a recommendation system that strives to offer guidance in selecting films. Depending on the method employed, recommendation systems can be categorized into three groups: collaborative filtering, content-based filtering, and hybrid filtering. In this work, collaborative filtering, one of the methods frequently used in recommendation systems was used. There are two ways to the Collaborative Filtering approach: User-Based Collaborative Filtering (UBCF) and Item-Based Collaborative Filtering (IBCF). There are two methods for finding similar items or users: Cosine and Pearson similarities. The Cosine similarity approach is one way to determine how similar two items are. Additionally, the Pearson Correlation Coefficient approach, which determines similarities between objects by calculating linear correlations between two sets, is the most widely employed. This study aims to determine which system produces the highest item similarity in IBCF and predicted ratings to actual ratings using 90% training and 10% testing data. The data set taken from MovieLens.org consists of 943 users from 1664 movies with 99392 ratings. The MovieLens data collection will be analyzed with the RStudio and the R package recommenderlab. The results reveal that the IBCF with Cosine similarities shows the number of items recommended n top-rated movies to each user for 10 movies. The IBCF can identify the most recommended films and creates a frequency distribution of items.
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Abstract The most important subjects in the memory‐based collaborative filtering recommender system (RS) are to accurately calculate the similarities between users and finally finding interesting recommendations for active users. The main purpose of this research is to provide a list of the best items for recommending in less time. The fuzzy‐genetic collaborative filtering (FGCF) approach recommends items by optimizing fuzzy similarities in the continuous genetic algorithm (CGA). In this method, first, the crisp values of user ratings are converted to fuzzy ratings, and then the fuzzy similarities are calculated. Similarity values are placed into the genes of the genetic algorithm, optimized, and finally, they are used in fuzzy prediction. Therefore, the fuzzy system is used twice in this process. Experimental results on RecSys, Movielens 100 K, and Movielens 1 M datasets show that FGCF improves the collaborative filtering RS performance in terms of quality and accuracy of recommendations, time and space complexities. The FGCF method is robust against the sparsity of data due to the correct choice of neighbours and avoids the users' different rating scales problem but it not able to solve the cold‐start challenge.
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Collaborative filtering algorithm is one of the most widely used algorithms in recommender systems and has demonstrated promising results. But it relies too much on similarity to find the nearest neighbors. Whatever, the trust between users is also an import factor needed to be considered. This paper proposed a collaborative filtering algorithm that combined the user similarity and trust to obtain a more appropriate nearest neighbors set. Users not only have same interests as their nearest neighbors, but also have higher level of acceptance in the items recom-mended by their nearest neighbors. Extensive experiments based on Film Trust and MovieLens datasets have shown that the approach has major potential in improving the accuracy of recommended item.
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Collaborative filtering is one of the most extensive and successful personalized recommendation algorithm in e-commerce recommendation system.Affected by data sparsity,the traditional collaborative filtering algorithms does not reflect the interest similarity of uses calculating similarity between users on the smaller set of common rated items accurately,seriously affecting the accuracy of recommendation system.To solve this problem,collaborative filtering algorithm based on co-ratings was proposed by analyzing the distribution of co-ratings and relationship between co-ratings and similarity,directly using co-ratings as a criterion to select nearest neighbor without calculating similarity.Experiments on MovieLens datasets show that the algorithm can make a substantial increase in prediction accuracy and recommendation coverage.
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In recent years, ranking-oriented collaborative filtering (CF) algorithms have achieved great success in recommender systems. They achieve advanced performance by predicting item preference ranking rather than the absolute value of the item. However, the listwise collaborative filtering (ListCF) only considers the impact of user ratings, ignores the influence of other feature factors, which leads to lower accuracy of recommendation. This paper proposes a listwise collaborative filtering algorithm based on user ratings and user-item type ratings. Experiments on Movielens proved the improvement of recommendation accuracy.
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Collaborative filtering (CF) algorithms have received a lot of interest in recommender systems due to their ability to give personalized recommendations by exploiting user-item interaction data. In this article, we explore two popular CF methods—K-Nearest Neighbors (KNN) Regression and Non-Negative Matrix Factorization (NMF)—in detail as we dig into the world of collaborative filtering. Our goal is to evaluate their performance on the MovieLens 1M dataset and offer information about their advantages and disadvantages. A thorough explanation of the significance of recommender systems in contemporary content consumption settings is given at the outset of our examination. We look into Collaborative Filtering's complexities and how it uses user choices to produce tailored recommendations. Then, after setting the scene, we explain the KNN Regression and NMF approaches, going over their guiding principles and how they apply to recommendation systems. We conduct an extensive investigation of KNN Regression and NMF on the MovieLens 1M dataset to provide a thorough evaluation. We describe the model training processes, performance measures, and data pre-processing steps used. We measure and analyse the predicted accuracy of these strategies using empirical studies, revealing light on their effectiveness when applied to various user preferences and content categories.
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협업여과는 추천시스템에서 널리 사용되는 기법으로 다른 사용자의 평가를 기반으로 아이템을 추천하는 기법이다. 사용자 데이터베이스를 이용하는 메모리기반 협업여과에는 사용자기반 기법과 아이템기반 기법이 있다. 사용자기반 협업여과는 유사한 선호도를 가지는 이웃사용자들의 선호도를 바탕으로 특정 아이템에 대한 선호도를 예측하는 반면, 아이템기반 협업여과는 아이템들의 유사도를 바탕으로 특정 사용자의 선호도를 예측한다. 본 논문에서는 추천의 성능을 향상시키기 위하여 이웃사용자와 이웃아이템 크기의 비율을 가중치로 하여 사용자기반 예측값과 아이템기반 예측값을 결합함으로써 최종 예측값을 생성하는 결합예측기법을 제안한다. MovieLens 데이터 셋과 BookCrossing 데이터 셋을 이용한 실험을 통해 본 논문에서 제안한 결합예측기법이 영화와 책에 대하여 사용자기반과 아이템기반보다 예측의 정확성을 향상시킴을 보인다. Collaborative filtering is a popular technique that recommends items based on the opinions of other people in recommender systems. Memory-based collaborative filtering which uses user database can be divided in user-based approaches and item-based approaches. User-based collaborative filtering predicts a user's preference of an item using the preferences of similar neighborhood, while item-based collaborative filtering predicts the preference of an item based on the similarity of items. This paper proposes a combined forecast scheme that predicts the preference of a user to an item by combining user-based prediction and item-based prediction using the ratio of the number of similar users and the number of similar items. Experimental results using MovieLens data set and the BookCrossing data set show that the proposed scheme improves the accuracy of prediction for movies and books compared with the user-based scheme and item-based scheme.
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