Preferences Of Similar Users Research Articles

A Cloud Service Selection Method Based on Trust and User Preference Clustering

In order to achieve universal and personalized cloud service choices in a social network environment, we propose a cloud service selection method based on trust and user preference clustering. The method performs a comprehensive trust evaluation, which is used to evaluate and select cloud services. Meanwhile, we propose an improved condensed hierarchical clustering method based on user preference similarity to further improve the accuracy of recommendation trust. A cloud model-based approach is used to measure similarities between users, and then a hierarchical clustering method is used to divide users into different domains according to user similarity. The final recommendation trust will be obtained, which includes the intra-domain recommendation trust and the extra-domain recommendation trust. The comprehensive trust of cloud services, which consists of direct trust and recommended trust. Simulation experiments verify the accuracy and superiority of the clustering algorithm. Experimental results show that the cloud service selection method improves the transaction success rate and enables users to select more satisfactory cloud services.

A recommender system to address the Cold Start problem for App usage prediction

The Cold Start Recommender System (RS) for App usage prediction on mobile phones is important for improving new user experience on mobile operating systems. At present, the existing Cold Start RS computes the probability of App launching mainly by mining the potential information of new users and similar users (i.e., collaborative filtering algorithm CF). But for newly installed Apps, the default CF does not have any useful information for App Cold Start recommendation, resulting in the data sparseness. To tackle the problems, we think that App usage periodicity installed by the new user is regularly followed under different conditions. It not only compensates for the data sparseness of similar users but also increases the predictive flexibility when the user’s environment changes. Therefore, we designed Predictor, an efficient dynamic CF fusion algorithm that provides App Cold Start prediction for new users on mobile devices. It dynamically combines both App preferences of similar users (user-based CF) and App usage periodicity (item-based CF) through the conditional combination. Compared to other traditional methods, Predictor proposes more appropriate App launching recommendation and matches the launching expectations of most users.

A Prediction Method of Mobile User Preference Based on the Influence between Users

User preference will be impacted by other users. To accurately predict mobile user preference, the influence between users is introduced into the prediction model of user preference. First, the mobile social network is constructed according to the interaction behavior of the mobile user, and the influence of the user is calculated according to the topology of the constructed mobile social network and mobile user behavior. Second, the influence between users is calculated according to the user’s influence, the interaction behavior between users, and the similarity of user preferences. When calculating the influence based on the interaction behavior, the context information is considered; the context information and the order of user preferences are considered when calculating the influence based on the similarity of user preferences. The improved collaborative filtering method is then employed to predict mobile user preferences based on the obtained influence between users. Finally, the experiment is executed on the real data set and the integrated data set, and the results show that the proposed method can obtain more accurate mobile user preferences than those of existing methods.

Mining Users’ Preference Similarities in E-commerce Systems Based on Webpage Navigation Logs

Mining users’ preference patterns in e-commerce systems is a fertile area for a great many application directions, such as shopping intention analysis, prediction and personalized recommendation. The web page navigation logs contain much potentially useful information, and provide opportunities for understanding the correlation between users’ browsing patterns and what they want to buy. In this article, we propose a web browsing history mining based user preference discovery method for e-commerce systems. First of all, a user-browsing-history-hierarchical-presentationgraph to established to model the web browsing histories of an individual in common e-commerce systems, and secondly an interested web page detection algorithm is designed to extract users’ preference. Finally, a new method called UPSAWBH (User Preference Similarity Calculation Algorithm Based on Web Browsing History), which measure the level of users’ preference similarity on the basis of their web page click patterns, is put forward. In the proposed UPSAWBH, we take two factors into account: 1) the number of shared web page click sequence, and 2) the property of the clicked web page that reflects users’ shopping preference in e-commerce systems. We conduct experiments on real dataset, which is extracted from the server of our self-developed e-commerce system. The results indicate a good effectiveness of the proposed approach.

Mining user preferences of new locations on location-based social networks: a multidimensional cloud model approach

In recent years, the prevalent of location-based social networks contributes massive data for location recommendation. Although collaborative filtering (CF) algorithm has been widely employed for location recommendation, it suffers the data sparsity and the high time complexity as it estimates the similarity of users by the common locations. In this paper, we extend the two-dimensional cloud model to the multidimensional cloud model and utilize it to the measure the similarity of user preferences and user behaviors. This method not only considers the multiple attributes of users (e.g., the diversity of user preferences), but also alleviates the sparsity of location recommendation based on CF algorithm to some extent. Then we integrate the similarity of user preferences, social ties and user behaviors into CF algorithm, which is expected to mine user preferences of new locations (MUPNL) more precisely. Furthermore, in order to improve the efficiency of the MUPNL algorithm, we parallelize it with Mapreduce framework. Experimental results on Yelp academic dataset demonstrate the good performance of the distributed MUPNL algorithm in accuracy and efficiency.

Hybrid collaborative filtering for high-involvement products: A solution to opinion sparsity and dynamics

High capital value goods that are purchased only after long and careful consideration, such as a car, truck, appliance (called high-involvement products) are increasingly being purchased online. For these products accurate recommendations are very important. Collaborative filtering (CF) is a commonly used approach for recommending products based on known preferences of similar users. Two challenges that limit the performance of CF in high-involvement products are the ratings' sparsity and dynamics. We use online reviews as an auxiliary information source and design a hybrid CF method to address the sparsity problem. Additionally, we consider the dynamics that may exist in online ratings and reviews. Specifically, we first investigate empirically the evolution of the ratings and reviews over time and sequence. The results show that there are both temporal and sequential dynamics in online ratings, and only temporal dynamics in online reviews. Next, considering these dynamics, we develop techniques to predict missing ratings based on online reviews and product similarities. The sparsity of the User–Item rating matrix is alleviated by filling these predicted ratings. The experiment, based on real-world datasets, demonstrates the superior performance of our recommendation approach for high-involvement products.

Similarity Analysis in Social Networks Based on Collaborative Filtering

Collaborative Filtering is of particular interest because its recommendations are based on the preferences of similar users. This allows us to overcome several key limitations. This paper explains the need for collaborative filtering, its benefits and related challenges. We have investigated several variations and their performance under a variety of circumstances. We also explored the implications of these results when weighing K Nearest Neighbor algorithm for implementation. Based on the relationship of individuals, putting forward a new incremental learning collaborative filtering recommendation system, discovery it is a better way to acquire optimum results.

Folksonomy-based user interest and disinterest profiling for improved recommendations: An ontological approach

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.

(Partial) user preference similarity as classification-based model similarity

Recommender systems play an important role in helping people finding items they like. One type of recommender system is collaborative filtering that considers feedback of like-minded people. The fundamental assumption of collaborative filtering is that people who previously shared similar preferences behave similarly later on. This paper introduces several novel, classification-based similarity metrics that are used to compare user preferences. Furthermore, the concept of partial preference similarity based on a machine learning model is presented. For evaluation the cold-start behavior of the presented classification-based similarity metrics is evaluated in a large-scale experiment. It is shown that classification-based similarity metrics with machine learning significantly outperforms other similarity approaches in different cold-start situations under different degrees of data-sparseness.

A genre-based fuzzy inference approach for effective filtering of movies

Recommendation techniques are utilized in electronic commerce because of their potential commercial value. Many e-commerce sites employ collaborative filtering techniques to provide recommendations to customers based on the preferences of similar users. However, as the number of customers and the range of products increase, the prediction accuracy of memory-based collaborative filtering algorithms declines because of sparse ratings. In addition, the time complexity of such algorithms is quite high in the prediction phase. To resolve these issues, we propose a genre-based fuzzy inference filtering approach for predicting movie preferences. We use content-based and collaborative filtering algorithms as baseline methods to evaluate the performance of our approach. The results of experiments demonstrate that the hybrid approach exploits the strengths of the content-based and collaborative filtering algorithms to achieve effective filtering in terms of precision. Moreover, the computation time can be reduced by using the α-cut approach. The findings have implications for the design of an interactive movie recommendation system for the World Wide Web.

Community-based user domain model collaborative recommendation algorithm

Collaborative Filtering (CF) is a commonly used technique in recommendation systems. It can promote items of interest to a target user from a large selection of available items. It is divided into two broad classes: memory-based algorithms and model-based algorithms. The latter requires some time to build a model but recommends online items quickly, while the former is time-consuming but does not require pre-building time. Considering the shortcomings of the two types of algorithms, we propose a novel Community-based User domain Collaborative Recommendation Algorithm (CUCRA). The idea comes from the fact that recommendations are usually made by users with similar preferences. The first step is to build a user-user social network based on users' preference data. The second step is to find communities with similar user preferences using a community detective algorithm. Finally, items are recommended to users by applying collaborative filtering on communities. Because we recommend items to users in communities instead of to an entire social network, the method has perfect online performance. Applying this method to a collaborative tagging system, experimental results show that the recommendation accuracy of CUCRA is relatively good, and the online time-complexity reduces to O(n).

Integrating Collaborative and Reclusive Methods for Effective Recommendations: A Fuzzy Bayesian Approach

Collaborative filtering (CF), the most successful and widely used technique, recommends items based on the preferences of similar users. The main potentials of CF are its cross-genre recommendation ability, and that it is completely independent of representation of the items being recommended. However, CF suffers from sparsity and cold start problems. On the other hand, a highly effective variant of content-based filtering (CBF), reclusive methods (RMs) based on the preference of the single individual for whom recommendations to be made, provides a methodology that considers uncertainty and the multivalued nature of item features as well as user preferences in a content-based framework using fuzzy logic approaches. The adoption of RM paradigm has several advantages when compared to CF such as sparsity and new item problem, but it suffers from overspecialization and limited content analysis. In view of the complementary nature of CF and RM, we develop a hybrid recommender system (RS) that helps in alleviating aforementioned problems in each approach. First, we propose fuzzy naïve Bayesian classifier based CF (FNB-CF) and RM (FNB-RM) for handling correlation-based similarity problems. To overcome individual weaknesses of FNB-CF and FNB-RM, we develop a hybrid RS, FNB-CF-RM. Effectiveness of our proposed hybrid RS is demonstrated through experimental results using the MovieLens and IMDb data sets.

Ontological Approach for Effective Generation of Concept Based User Profiles to Personalize Search Results

Problem statement: Ontological user profile generation was a semantic approach to derive richer concept based user profiles. It depends on the semantic relationship of concepts. This study focuses on ontology to derive concept oriented user profile based on user search queries and clicked documents.This study proposes concept based on topic ontology which derives the concept based user profiles more independently. It was possible to improve the search engine processes more efficiently. Approach: This process consists of individual user’s interests, topical categories of user interests and identifies the relationship among the concepts. The proposed approach was based on topic ontology for concept based user profile generation from search engine logs. Spreading activation algorithm was used to optimize the relevance of search engine results. Topic ontology was constructed to identify the user interest by assigning activation values and explore the topics similarity of user preferences. Results: To update and maintain the interest scores, spreading activation algorithm was proposed. User interest may change over the period of time which was reflected to user profiles. According to profile changes, search engine was personalized by assigning interest scores and weight to the topics. Conclusion: Experiments illustrate the efficacy of proposed approach and with the help of topic ontology user preferences can be identified correctly. It improves the quality of the search engine personalization by identifying the user’s precise needs.