Recommendation Framework Combining User Interests with Fashion Trends in Apparel Online Shopping
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Although fashion-related products account for most of the online shopping categories, it becomes more difficult for users to search and find products matching their taste and needs as the number of items available online increases explosively. Personalized recommendation of items is the best method for both reducing user effort on searching for items and expanding sales opportunity for sellers. Unfortunately, experimental studies and research on fashion item recommendation for online shopping users are lacking. In this paper, we propose a novel recommendation framework suitable for online apparel items. To overcome the rating sparsity problem of online apparel datasets, we derive implicit ratings from user log data and generate predicted ratings for item clusters by user-based collaborative filtering. The ratings are combined with a network constructed by an item click trend, which serves as a personalized recommendation through a random walk. An empirical evaluation on a large-scale real-world dataset obtained from an apparel retailer demonstrates the effectiveness of our method.Recommender systems provide users with product information and suggestions, which has gradually become an important research tool in e-commerce IT technology, which has attracted a lot of attention of researchers. Collaborative filtering recommendation technology has been the most successful recommendation technology so far, but there are two major problems-recommendation quality and scalability. At present, research at home and abroad mainly focuses on recommendation quality, and there is less discussion on scalability. The scalability problem is that as the size of the system increases, the response time of the system increases to a point where users cannot afford it. Existing solutions often result in a significant drop in recommendation quality while reducing recommendation response time. In this paper, the clustering analysis subsystem based on the genetic algorithm is innovatively introduced into the traditional collaborative filtering recommendation system, and its design and implementation are given. In addition, when obtaining the nearest neighbors, only the clustered users of the target user are searched, making it a collaborative filtering recommender system based on genetic clustering. The experimental results show that the response time of the traditional collaborative filtering recommender system increases linearly with the increase in the number of users while the response time of the collaborative filtering recommender system based on genetic clustering remains unchanged with the increase in the number of users. On the other hand, the recommendation quality of the collaborative filtering recommender system based on genetic clustering is basically not degraded compared with that of the traditional collaborative filtering recommender system. Therefore, the collaborative filtering recommender system based on genetic clustering can effectively solve the scalability problem of the collaborative filtering recommender system.
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Recommender systems can find user interested information based on the information filtering algorithms. Collaborative filtering technique has been proved to be one of the most successful techniques in recommender systems. And there are two approaches: one is user-based collaborative filtering and the other is item-based collaborative filtering. Data sparsity is the main problem in recommender system, which leads to the bad accuracy. To solve the sparsity problem, this paper presents a personalized recommendation algorithm joining case-based reasoning and item-based collaborative filtering. At first, it employs case-based reasoning technology to fill the vacant ratings of the user-item matrix. And then, it produces prediction of the target user to the target item using item-based collaborative filtering. The recommendation algorithm combining the case-based reasoning and item-based collaborative filtering can alleviate the sparsity issue and can produce more accuracy recommendation than the traditional recommender systems.
Information filtering system
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The use of Collaborative Filtering is becoming very popular in designing a simple yet efficient recommender system. A recommender system based on Collaborative Filtering basically predicts a user's interest in some item on the basis of the scores generated and the correlation calculated between the users. In this paper we propose a basic structure and steps of designing a recommender system that uses Collaborative Filtering (user based) along with applications of partitioning and clustering of data, thus designing a Restaurant Recommender System. The proposed system reduces the complexity and gives a clear view of the basic approach to build a recommender system from scratch.
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The proliferation use of Internet has made people to rely on virtual recommendations. Recommender systems help out in giving important recommendations. Collaborative filtering is the most successful and widely used approach in designing recommender systems since the introduction of the concept of recommender systems. This approach uses the known tastes and preferences of a set of users to make predictions or generate recommendations about the unknown tastes and preferences of the target user. This paper discusses various works which use collaborative filtering approach to design recommender systems. The paper also gives a comparison of these approaches.
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Recommender systems have achieved widespread success for e-commerce companies. Significant growth of customers and products poses key challenges for recommender system namely sparsity and scalability. In this paper, a hybrid system is proposed that is capable of handling these issues that is based on collaborative filtering and fuzzy c-means clustering algorithms. Experimental results show the effectiveness of the proposed recommender system.
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Information filtering system
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Recommender systems help to overcome the problem of information overload on the Internet by providing personalized recommendations to the users. Content-based filtering and collaborative filtering are usually applied to predict these recommendations. Among these two, Collaborative filtering is the most common approach for designing e-commerce recommender systems. Two major challenges for CF based recommender systems are scalability and sparsity. In this paper we present an incremental clustering approach to improve the scalability of collaborative filtering.
Information Overload
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The rating and review based algorithms like collaborative filtering faces the cold start problem on addition of new items or new users in the recommender system because of lack of ratings and reviews. The primary aim of this work is to solve the cold start problem faced by rating based recommendation system and also increase the accuracy of recommender systems. We used the content-based filtering algorithm along with the cross-domain data taken from Facebook so that the problem of addition of a new user problem and a new item in recommender system can be solved. The combination of both content-based filtering and cross-domain data obtains better results than the collaborative filtering and shows a good way to deal with the cold start problem.
Cold start (automotive)
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Recommender system becomes very popular and has important role in an information system or webpages nowadays. A recommender system tries to make a prediction of which item a user may like based on his activity on the system. There are some familiar techniques to build a recommender system, such as content-based filtering and collaborative filtering. Content-based filtering does not involve opinions from human to make the prediction, while collaborative filtering does, so collaborative filtering can predict more accurately. However, collaborative filtering cannot give prediction to items which have never been rated by any user. In order to cover the drawbacks of each approach with the advantages of other approach, both approaches can be combined with an approach known as hybrid technique. Hybrid technique used in this work is weighted technique in which the prediction score is combination linear of scores gained by techniques that are combined.The purpose of this work is to show how an approach of weighted hybrid technique combining content-based filtering and item-based collaborative filtering can work in a movie recommender system and to show the performance comparison when both approachare combined and when each approach works alone. There are three experiments done in this work, combining both techniques with different parameters. The result shows that the weighted hybrid technique that is done in this work does not really boost the performance up, but it helps to give prediction score for unrated movies that are impossible to be recommended by only using collaborative filtering.
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