Supervised learning-based collaborative filtering using market basket data for the cold-start problem

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A collaborative filtering similarity measure based on singularities.

Recommender systems play an important role in reducing the negative impact of informa- tion overload on those websites where users have the possibility of voting for their prefer- ences on items. The most normal technique for dealing with the recommendation mechanism is to use collaborative filtering, in which it is essential to discover the most similar users to whom you desire to make recommendations. The hypothesis of this paper is that the results obtained by applying traditional similarities measures can be improved by taking contextual information, drawn from the entire body of users, and using it to cal- culate the singularity which exists, for each item, in the votes cast by each pair of users that you wish to compare. As such, the greater the measure of singularity result between the votes cast by two given users, the greater the impact this will have on the similarity. The results, tested on the Movielens, Netflix and FilmAffinity databases, corroborate the excellent behaviour of the singularity measure proposed

Preference Mining Using Neighborhood Rough Set Model on Two Universes

Preference mining plays an important role in e-commerce and video websites for enhancing user satisfaction and loyalty. Some classical methods are not available for the cold-start problem when the user or the item is new. In this paper, we propose a new model, called parametric neighborhood rough set on two universes (NRSTU), to describe the user and item data structures. Furthermore, the neighborhood lower approximation operator is used for defining the preference rules. Then, we provide the means for recommending items to users by using these rules. Finally, we give an experimental example to show the details of NRSTU-based preference mining for cold-start problem. The parameters of the model are also discussed. The experimental results show that the proposed method presents an effective solution for preference mining. In particular, NRSTU improves the recommendation accuracy by about 19% compared to the traditional method

A collaborative filtering approach to mitigate the new user cold start problem.

The new user cold start issue represents a serious problem in recommender systems as it can lead to the loss of new users who decide to stop using the system due to the lack of accuracy in the recommenda- tions received in that first stage in which they have not yet cast a significant number of votes with which to feed the recommender system?s collaborative filtering core. For this reason it is particularly important to design new similarity metrics which provide greater precision in the results offered to users who have cast few votes. This paper presents a new similarity measure perfected using optimization based on neu- ral learning, which exceeds the best results obtained with current metrics. The metric has been tested on the Netflix and Movielens databases, obtaining important improvements in the measures of accuracy, precision and recall when applied to new user cold start situations. The paper includes the mathematical formalization describing how to obtain the main quality measures of a recommender system using leave- one-out cross validation

A hybrid recommendation approach for a tourism system

Many current e-commerce systems provide personalization when their content is shown to users. In this sense, recommender systems make personalized suggestions and provide information of items available in the system. Nowadays, there is a vast amount of methods, including data mining techniques that can be employed for personalization in recommender systems. However, these methods are still quite vulnerable to some limitations and shortcomings related to recommender environment. In order to deal with some of them, in this work we implement a recommendation methodology in a recommender system for tourism, where classification based on association is applied. Classification based on association methods, also named associative classification methods, consist of an alternative data mining technique, which combines concepts from classification and association in order to allow association rules to be employed in a prediction context. The proposed methodology was evaluated in some case studies, where we could verify that it is able to shorten limitations presented in recommender systems and to enhance recommendation quality

A model of an adaptive system for recommending learning objects in a constructivist learning environment

Computer-based multimedia learning environments support the idea that people learn better and more deeply when appropriate pictures (i.e., animations, video, static graphics) are added to text or narration. There are many adaptive learning systems that adapt learning materials to student properties, preferences, and activities. Adaptive learning environments mostly support only traditional concepts of learning. There is a need to design and develop an e-learning system that embodies principles of constructivist learning approach. The solution is in recommenders systems, which suggest items of interest to users based on their preferences (i.e. previous ratings). If there are no ratings for a certain user or item/object, there is a situation called a cold start problem, which leads to unreliable recommendations. Researchers mostly avoid tackling the absolute cold start in recommender systems. The topic of presented dissertation is designing a recommender system with a novel approach to avoid cold start problem. Approaches for solving the new user cold start problem can be divided into two main groups: the first group performs additional inquiries to gather more information about the users; and the second group uses dedicated algorithms for users in the cold start state. The first group of approaches aims at performing additional inquiries about the user. According to this approach, we relate combinations of different learning styles (taking into account four different learning styles models) to preferred multimedia types. We explore a decision model aimed at proposing learning material of an appropriate multimedia type. The study includes 272 student participants. The resulting decision model shows that students prefer well-structured learning texts with colour discrimination, and that the hemispheric learning style model is the most important criterion in deciding student preferences for different multimedia learning materials. To provide a more accurate and reliable model for recommending different multimedia types more learning style models must be combined. Kolb’s classification and the VAK classification allow us to learn if students prefer an active role in the learning process, and what multimedia type they prefer. The results also shows that there is an obvious need to combine learning styles model in order to get a wider view of the student’s characteristics: an approach to problem solving problems, cognitive modes, way of thinking, and a dominant mode of perceiving information. On another hand, model recommends same multimedia material regardless of the learning topic. In the second part of our research, we have designed and developed a novel approach for alleviating the cold start problem by imputing missing values into the input matrix, thereby improving recommendation performance. Our approach has three steps: 1) finding similar users to given user in cold start state; 2) selecting relevant attributes for the imputation process; 3) aggregate ratings to input matrix for a user in the cold start state. We separate our approach for solving cold start problem into solving absolute cold start problem and solving partial cold start problem. According to the results of our experiments (solving absolute cold start problem), the results indicate that all our proposed methods improve recommending for non-negative matrix factorization with stochastic gradient descent (NG). For semi-non-negative matrix factorization with missing data (SN), combinations FR-ME (imputing attribute's mean value into the attributes that have the highest frequency of the most frequent values) and SD-MF (imputing attribute’s most frequent value into attributes that have the lowest standard deviation) improve recommendations for users in the absolute cold start state. For non-negative matrix factorization with alternating least squares (NS) and matrix factorization by data fusion (DF), none of variations of proposed parameters (methods) improves recommending in absolute cold start state. In the next stage of our research, we evaluated our approach for solving partial cold start problem. Statistical analysis of experimental evaluation of our approach on the artificial domain showed that each parameter significantly improved recommending of matrix factorization methods. The methods that yield improvements in recommendation accuracy compared with the raw matrix factorization are methods that consider 25 % of similar users ($25$-*-*-*), select an attribute according to the frequency (*-FR-*-*) or RReliefF (*-RR-*-*), and impute a value aggregated by mean value (ME) or predicted by using regression trees (RT). For further investigation we chose two method combinations (25-FR-ME-* and 25-RR-RT-*), which were expected to work well, and compared them with other strategies on real domains. Among all approaches evaluated on the artificial domain, we chose the best performing method with the highest average rank – a method that considers 50 % of similar users, selects an attribute for imputation according to the RReliefF, and imputes a value predicted by linear regression (50-RR-LR-*). All three combinations of the selected methods were evaluated on two real domains: Jester in PEFbase. An evaluation showed that method 25-FR-ME-* combined with matrix factorization NG performed statistically better than the raw matrix factorization algorithms (DF, NG, NS in SN) on real domains for users in the partial cold-start state. The results demonstrated the advantage of using imputation approaches in terms of better recommendation accuracy. At the same time, the results have shown that imputing of missing values has no negative impact for recommending to the users, which are not in the cold start state

Optimizing E-Management Using Web Data Mining

Today, one of the biggest challenges that E-management systems face is the explosive growth of operating data and to use this data to enhance services. Web usage mining has emerged as an important technique to provide useful management information from user's Web data. One of the areas where such information is needed is the Web-based academic digital libraries. A digital library (D-library) is an information resource system to store resources in digital format and provide access to users through the network. Academic libraries offer a huge amount of information resources, these information resources overwhelm students and makes it difficult for them to access to relevant information. Proposed solutions to alleviate this issue emphasize the need to build Web recommender systems that make it possible to offer each student with a list of resources that they would be interested in. Collaborative filtering is the most successful technique used to offer recommendations to users. Collaborative filtering provides recommendations according to the user relevance feedback that tells the system their preferences. Most recent work on D-library recommender systems uses explicit feedback. Explicit feedback requires students to rate resources which make the recommendation process not realistic because few students are willing to provide their interests explicitly. Thus, collaborative filtering suffers from “data sparsity” problem. In response to this problem, the study proposed a Web usage mining framework to alleviate the sparsity problem. The framework incorporates clustering mining technique and usage data in the recommendation process. Students perform different actions on D-library, in this study five different actions are identified, including printing, downloading, bookmarking, reading, and viewing the abstract. These actions provide the system with large quantities of implicit feedback data. The proposed framework also utilizes clustering data mining approach to reduce the sparsity problem. Furthermore, generating recommendations based on clusters produce better results because students belonging to the same cluster usually have similar interests. The proposed framework is divided into two main components: off-line and online components. The off-line component is comprised of two stages: data pre-processing and the derivation of student clusters. The online component is comprised of two stages: building student's profile and generating recommendations. The second stage consists of three steps, in the first step the target student profile is classified to the closest cluster profile using the cosine similarity measure. In the second phase, the Pearson correlation coefficient method is used to select the most similar students to the target student from the chosen cluster to serve as a source of prediction. Finally, a top-list of resources is presented. Using the Book-Crossing dataset the effectiveness of the proposed framework was evaluated based on sparsity level, and Mean Absolute Error (MAE) regarding accuracy. The proposed framework reduced the sparsity level between (0.07% and 26.71%) in the sub-matrices, whereas the sparsity level is between 99.79% and 78.81% using the proposed framework, and 99.86% (for the original matrix) before applying the proposed framework. The experimental results indicated that by using the proposed framework the performance is as much as 13.12% better than clustering-only explicit feedback data, and 21.14% better than the standard K Nearest Neighbours method. The overall results show that the proposed framework can alleviate the Sparsity problem resulting in improving the accuracy of the recommendations