Folksonomy-based user interest and disinterest profiling for improved recommendations: An ontological approach
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Social tagging has revolutionized the social and personal experience of users across numerous web platforms by enabling the organizing, managing, sharing and searching of web data. The extensive amount of information generated by tagging systems can be utilized for recommendation purposes. However, the unregulated creation of social tags by users can produce a great deal of noise and the tags can be unreliable; thus, exploiting them for recommendation is a nontrivial task. In this study, a new recommender system is proposed based on the similarities between user and item profiles. The approach applied is to generate user and item profiles by discovering tag patterns that are frequently generated by users. These tag patterns are categorized into irrelevant patterns and relevant patterns which represent diverse user preferences in terms of likes and dislikes. Furthermore, presented here is a method for translating these tag-based profiles into semantic profiles by determining the underlying meaning(s) of the tags, and mapping them to semantic entities belonging to external knowledge bases. To alleviate the cold start and overspecialization problems, semantic profiles are enriched in two phases: (a) using a semantic spreading mechanism and then (b) inheriting the preferences of similar users. Experiment indicates that this approach not only provides a better representation of user interests, but also achieves a better recommendation result when compared with existing methods. The performance of the proposed recommendation method is investigated in the face of the cold start problem, the results of which confirm that it can indeed remedy the problem for early adopters, hence improving overall recommendation quality.Keywords:
Folksonomy
Profiling (computer programming)
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Information Overload
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Recommender systems have become extremely popular in recent years due to their ability to predict a user's preference or rating of a certain item by analyzing similar users in the network.Trust-based recommender systems generate these predictions by using an explicitly issued trust between the users.In this paper we propose a recommendation algorithm called Averaged Localized Trust-Based Ant Recommender (ALT-BAR) that follows the methodology applied by Ant Colony Optimization algorithms to increase the accuracy of predictions in recommender systems, especially for cold start users.Cold start users are considered challenging to deal with in any recommender system because of the few ratings they have in their profiles.ALT-BAR reinforces the significance of trust between users, to overcome the lack of ratings, by modifying the way the initial pheromone levels of edges are calculated to reflect each edge's associated trust level.An appropriate initialization of pheromone in ant algorithms in general can guarantee a proper convergence of the system to the optimal solution.ALT-BAR's approach allows the ants to expand their search scope in the solution space to find ratings for cold start users while exploiting discovered good solutions for the sake of heavy raters.When compared to other algorithms in the literature, ALT-BAR proved to be extremely successful in enhancing the prediction accuracy and coverage for cold start users while still maintaining fairly good results for heavy raters.
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Recommender systems aim to provide users with personalised recommendations of items based on their preferences. Such systems have during the last 15 years been applied in many domains and have enjoyed an increased popularity both in research communities and commerce. In this thesis our overlying aim is to work towards creating a recommender system for tourists visiting Trondheim. We begin this work by addressing the cold-start user problem, which is the problem of giving high-quality recommendations to new users who the system has little or no information about. The problem is severe in the tourist domain where the majority of users are cold-start users. To properly address the problem, we present a systematic literature review of the recommender system literature identifying nine types of solutions to the cold-start user problem. We evaluate the solution types in context of the tourist domain, and find that using demographic user data is the best solution in this domain. We include this solution as a part when we propose a design of a location-aware Bayesian recommender system for tourists visiting Trondheim.
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Recommender Systems have been very common and useful nowadays, for predictions of different items which facilitate user by giving suitable recommendations. It deals with the specific type of items and technique used to generate the recommendations that are customized to provide valuable and effective suggestions to the end user. The present system considers two well-known problems during recommendation such as cold start and data sparsity and resolved these problems to the great extend with high accuracy. The proposed system provides the recommendation to new user, with high reliability and accuracy values as shown in our result.
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Recommender systems are used to help users discover the items they might be interested in, especially when the number of alternatives is big. In modern streaming websites for music, movies, and TV-shows, E-commerce, social networks, and more, recommender systems are widely used. These recommender systems are often looking at the ratings on items for the current and other users, and predicting a rating on the items the user have not seen. Others match the content of an item itself against a user profile. A mix of the two is often used to make the predictions more accurate, and this can also help to the problem when a new user sign up where we have no knowledge about him. This issue, is a well-known problem for recommender systems often described as the cold-start problem, and much research has been done to find the best way to overcome this. In this thesis, we look at previous approaches to recommender systems and the cold-start problem in particular. We have developed our application, Eatelligent, which is recommending dinner recipes based on our study of previous research. Eatelligent has been designed to examine how we can approach the cold-start problem e ciently in a real world application, and what kind of feedback we can collect from the users.
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Recommender system is an applicable technique in most E-commerce commercial product technical designs. However, nearly all recommender system faces a challenge called the cold-start problem. The problem is so notorious that almost every industrial practitioner needs to resolve this issue when building recommender systems. Most cold-start problem solvers need some kind of data input as the starter of the system. On the other hand, many real-world applications place popular items or random items as recommendation results. In this paper, we propose a new technique called ZeroMat that requries no input data at all and predicts the user item rating data that is competitive in Mean Absolute Error and fairness metric compared with the classic matrix factorization with affluent data, and much better performance than random placement.
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Many e-commerce websites use recommender systems to recommend items to users. When a user or item is new, the system may fail because not enough information is available on this user or item. Various solutions to this `cold-start problem' have been proposed in the literature. However, many real-life e-commerce applications suffer from an aggravated, recurring version of cold-start even for known users or items, since many users visit the website rarely, change their interests over time, or exhibit different personas. This paper exposes the `Continuous Cold Start' (CoCoS) problem and its consequences for content- and context-based recommendation from the viewpoint of typical e-commerce applications, illustrated with examples from a major travel recommendation website, Booking.com. Keywords: Recommender systems, continous cold-start problem, industrial applications
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Persona
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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.
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There is a substantial increase in demand for recommender systems which have applications in a variety of domains. The goal of recommendations is to provide relevant choices to users. In practice, there are multiple methodologies in which recommendations take place like Collaborative Filtering (CF), Content-based filtering and Hybrid approach. For this paper, we will consider these approaches to be traditional approaches. The advantages of these approaches are in their design, functionality and efficiency. However, they do suffer from some major problems such as data sparsity, scalability and cold start to name a few. Among these problems, cold start is an intriguing area which has been plaguing recommender systems. Cold start problem occurs when the recommender system is not able to recommend new users/items since there is data sparsity. Researchers have formulated innovative techniques to alleviate cold start and the existing research conducted in this area is tremendous since the problem materializes in different use cases. Cold start is categorized into three problems. The first problem is when new users needs product recommendations from the system. The second problem is when new products listed in the system need to be recommended to existing users. The last problem is when new users and new products are present and the recommender engine needs to generate relevant recommendations. In this thesis, we concentrate on the first problem, where a user who is completely new to the system needs quality recommendations. We use a movie recommendation platform as our use case to analyze user demographics and find similarities between existing and new users to produce relevant recommendations.
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As one of the major challenges, cold-start problem plagues nearly all recommender systems. In particular, new items will be overlooked, impeding the development of new products online. Given limited resources, how to utilize the knowledge of recommender systems and design efficient marketing strategy for new items is extremely important. In this paper, we convert this ticklish issue into a clear mathematical problem based on a bipartite network representation. Under the most widely used algorithm in real e-commerce recommender systems, the so-called item-based collaborative filtering, we show that to simply push new items to active users is not a good strategy. Interestingly, experiments on real recommender systems indicate that to connect new items with some less active users will statistically yield better performance, namely, these new items will have more chance to appear in other users' recommendation lists. Further analysis suggests that the disassortative nature of recommender systems contributes to such observation. In a word, getting in-depth understanding on recommender systems could pave the way for the owners to popularize their cold-start products with low costs.
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Representation
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