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Collaborative filtering (CF), a representative algorithm of recommendation systems, is a method of using information of the neighbors of active user. The main idea of CF is that users who agreed in the ratings of certain items are likely to agree again in new items. The degree to which the two users' tendencies in the ratings of the co-rated items are consistent is measured using a similarity measure. Therefore, the similarity measure in CF plays a key role in the extraction of the representative neighbors. Studies on the improvement of similarity indicators for selecting representative neighbors are still ongoing. Recently, a new similarity measure, named OS, was proposed to enhance the recommendation performance by utilizing mathematical equations, such as the integral equation, system of linear differential equations, and non-linear systems. This study aims to understand the limitations of OS and overcome these limitations using the proposed method. In the proposed method, a sigmoid function was used to reflect preferences, such as the positive or negative sentiment of user ratings. In addition, to consider the absolute score difference, some of the formulas were modified, and finally, the performance improvement of the recommendation system was proved through experiments. [ABSTRACT FROM AUTHOR]

Recommendation systems provide reliable and relevant recommendations to users and also enable users' trust on the website. This is achieved by the opinions derived from reviews, feedbacks and preferences provided by the users when the product is purchased or viewed through social networks. This integrates interactions of social networks with recommendation systems which results in the behavior of users and user's friends. The techniques used so far for recommendation systems are traditional, based on collaborative filtering and content based filtering. This paper provides a novel approach called User-Opinion-Rating (UOR) for building recommendation systems by taking user generated opinions over social networks as a dimension. Two tripartite graphs namely User-Item-Rating and User-Item-Opinion are constructed based on users' opinion on items along with their ratings. Proposed approach quantifies the opinions of users and results obtained reveal the feasibility. [ABSTRACT FROM AUTHOR]

High Utility Itemset Mining (HUIM) is playing an important role in extracting meaningful knowledge that is profitable itemsets rather than their occurrence frequency. HUIM result thrives into a huge number of utility itemsets that makes it difficult in decision policy. The alternative procedure is condensed representation of HUI's that contains utility itemsets without any redundancy. Closed High Utility Itemsets (CHUI) and High Utility Generators (HUG) are lossless utility itemsets useful in recommendation systems. In this paper, we address how to extract Closed and Generator HUI's from the given dataset. For faster accessing, it is proposed hash based technique that store itemsets with the frequency, utility value, closed flag, and generator flag. Experimental results shows that it outperforms other approaches in time and space. [ABSTRACT FROM AUTHOR]

The heightening in the available information in the form of digital data and the number of users on the Internet have engendered a challenge of overburden of data which obstructs access to interested item on the Internet timely. There are many information retrieval systems which try to solve the problem of information overloading but in their cases prioritization and personalization of information were absent. The main aim is to develop a recommender system using item based collaborative filtering technique and K-means. The most popular algorithm in the recommender system's field is the collaborative filtering technique. Recommender systems are the filtering systems for information that concerned with the problem of information overburden by filtering essential information fragment out of enormous dynamically promoted information according to person's attentiveness, taste and distinguished behavior about them. We are considering m users, n items (in numbers) and presenting a model to fabricate a recommendation for the mobile user by a new approach. [ABSTRACT FROM AUTHOR]

Web growth, especially in social networks, is continuously increasing every day. Multiplicity of products offered and web pages has made picking up relevant items a tedious job. On the other hand, different tastes and behaviors of users is creating the probability to find a similar user among a large group of users difficult. As a result, automated software systems have difficulty to discover what is interesting to users. We have proposed a new approach to adapt to this flow. We will exploit domain knowledge of training data set to create a summary matrix. The summary matrix consists of new and few columns according to the attribute values of the selected feature. We fill the summary matrix with the average ratings based on the number of times that the attribute values appear in the user's profile for rated items. We use the summary matrix in two hybrid recommender systems. In our approach, we use meta-level technique which is one of the pipelined hybridization techniques. The proposed approach will reduce the effects of sparsity, cold start, and scalability which are common problems with the collaborative recommender systems. Furthermore, the proposed approach will improve the recommendation accuracy when there is comparison with the Collaborative Filtering Pearson Correlation approach and it will be faster as well. [ABSTRACT FROM AUTHOR]

We concern the issue of preference recommendation towards more reliable recommender system. General recommender system provides a collection of items or several bests of them, on the basis of a fixed preference constraint. However, the realistic preference from users may be such complicated that makes conventional recommender system unreliable. In real-world applications, e.g. travel planning or hotel selection, specific constraints may be involved like a limited travel time or an appropriate budget for hotel accommodation. Motivated by these applications, we investigate the constrained preference recommendation (CPR), in which two main types are studied including Threshold-CPR (T-CPR) and Range-CPR (R-CPR).We firstly analyze and define the related problems. Then, we provide solutions with illustration of problem-solving procedure. The results are analogized with other representative techniques. Finally, we provide an extension study on the general CPR problems. Therein, we firstly provide a normalized system for constraint representation. Secondly, we utilize the proposed normalized system to analyze representative literature. [ABSTRACT FROM AUTHOR]

Collaborative filtering (CF) is one of the most successful and effective recommendation techniques for personalized information access. This method makes recommendations based on past transactions and feedback from users sharing similar interests. However, many commercial recommender systems are widely adopting the CF algorithms; these methods are required to have the ability to deal with sparsity in data and to scale with the increasing number of users and items. The proposed approach addresses the problems of sparsity and scalability by first clustering users based on their rating patterns and then inferring clusters (neighborhoods) by applying two knowledge-based techniques: rule-based reasoning (RBR) and case-based reasoning (CBR) individually. Further to improve accuracy of the system, HRC (hybridization of RBR and CBR) procedure is employed to generate an optimal neighborhood for an active user. The proposed three neighborhood generation procedures are then combined with CF to develop RBR/CF, CBR/CF, and HBR/CF schemes for recommendations. An empirical study reveals that the RBR/CF and CBR/CF perform better than other state-of-the-art CF algorithms, whereas HRC/CF clearly outperforms the rest of the schemes. [ABSTRACT FROM AUTHOR]

This paper proposes task-oriented content-based recommendation for cross service recommendation. The proposed method has two features: one is that task-based features are automatically mined from the web, second is that it estimates user's intention on task, which means "what user wants to do" and "what problem user has" by task-based profile representation. We evaluate improvement of recommendation accuracy by user evaluation, in which we collect ratings on variety of contents (i.e. mobile web content, TV programs, restaurants, sightseeing spots, and hotels) from 1,859 people and conduct cross validation. In an experiment, the combination of task-based profile representation and term-based representation yields a 17.7% improvement in MAE (Mean Absolute Error) compared to term-based profile representation only or domain (content category) based profile representation only. [ABSTRACT FROM AUTHOR]

This paper proposes a content-based recommendation method tuned for multiple content domains. Most content-based recommender systems use content attributes (e.g. description of content and category of content) to represent items and user profiles. This raises the problem that when recommending content from new content domain, the recommendation quality will be poor until the item profile representation is updated through the input of a sufficient quantity of contents in the new content domain. In this paper, to develop an item profile representation that is independent of the contents, we propose a method that uses common categories to represent item profiles; it focuses on the real world activities (tasks) that attract the user's interest. Concretely, we present a light-weight task-model that covers a wide variety of tasks and yields item profiles. We also create a recommendation algorithm by incorporating the proposed profile representation into a statistical SVM(Support Vector Machine) based recommendation algorithm. Finally, we conduct a user test and the results of this test show 9% higher user evaluation scores of content recommendations compared to an existing content-based recommendation algorithm using term-based profile representation. This shows the effectiveness of our user-centered profile representation approach. [ABSTRACT FROM AUTHOR]