In recent years, music has become increasingly universal due to technological advances. All kinds of music have become more complex and a large amount around us. How recommending the music that user is interested in from a wide variety of music is the development intentions of the music recommendation system MRS (Music Recommendation System). In the recommending system, the most widely known is Content-based (CB) and Collaborative (COL). Chen et al. have proposed an alternative way that used CB and COL of music recommendation. The purpose of the CB method is to recommend the music objects that belong to the music groups the user is recently interested in. Each transaction is assigned a different weight, where the latest transaction has the highest weight. The preferences of users are derived from the access histories and recorded in profiles. Based on the collaborative approach, the purpose of the COL method is to provide unexpected findings due to the information sharing between relevant users. But in the CB method, the formula of computing music group weight pays much attention to the weight of the transaction. This will lead to the result that the group weight of music group B which appears once in the later transaction is larger than the group weight of the music group A which appears many times in the earlier transaction. In the COL method, they do not care the density of the group, where high density means that the transactions which the music group appears are close in the access history of the user. This will lead to the result that the supports of the groups which have different densities are the same, and then the users may be grouped together. Therefore, in this thesis, we propose the TICI (Transaction-Interest-Count-Interest) method to improve the CB method. Considering the two situations of the music group that user is interested in, the large count of music group and the appearance in the later transaction, we put two parameters: Count-Interest and Transaction-Interest in our TICI method to let users choose which weight they want to emphasize. Sometimes, people not only want the music object from one group. We extend the TICI method to find the group pair that the user is interested in. We use two thresholds: CountT and WeightT to decide which candidates can be in the large itemset. In our propose method, we have two possible ways to find the result. And we propose the DI (Density-Interest) method to improve the COL method. Our DI method calculates the supports of music groups and consider the distributions of appearances of the music group. From our simulation results, we show that our TICI method could provide better performance than the CB method. Moreover, our DI method also could provide better performance than the COL method.

With the extension of the World Wide Web, more than 7 million new pages being exploited each day, the problem of information overload due to a large number of coping, spreading and sharing causes the decreasing information quantity, diverse format of information and low quality of information. More researchers research what methods including search and recommendation can help users gather the critical topics. Whatever methods use historical purchasing or browsing data to find the proper information usually, can not considering all attributes affecting decision results. Therefore, the research uses Analytic Hierarchy Process belonging Multiple Critical Decision Theory to develop recommendation system. Providing a single, easy understand model, using hierarchic structuring reflecting the nature tendency of the mind, considering all attributes affecting decision results, Analytic Hierarchy Process takes into consideration the relative priorities and select the best alternative. Furthermore, it can show the subjective consciousness of the user in a structure way and assist designer to determine a more rational and conformable judgment. Based on Analytic Hierarchy Process, the commerce recommendation system expects to help user to find more satisfactory merchandise. It is found that the recommendation system using Analytic Hierarchy Process finds the accurate products for user and gets the higher satisfaction. However, the operation satisfaction is not higher that rank-based but still in available and satisfaction scope.

Literature digital libraries are the source of digitalized literature data, from which Researchers can search for articles that meet their personal interest. However, Users often confused by the large number of articles stored in a digital library and a single query will typically yield a large number of articles, among which only a small subset will indeed interest the user. To provide more effective and efficient information search, many systems are equipped with a recommendation subsystem that recommends articles that users might be interested. In this thesis, we aim to research a number of recommendation techniques for making personalized recommendation. In light of the previous work that used collaborative approach for making recommendation for literature digital libraries, in this thesis, we first propose three content-based recommendation approaches, followed by a set of hybrid approaches that combine both content-based and collaborative methods. These alternatives and approaches were evaluated using the web log of an operational electronic thesis system at NSYSU. It has been found the hybrid approaches yields better quality of articles recommendation.

No description available.

Recommendation systems serve to users of e-commerce applications for individual recommendations to certain products or services based on their preferences. The aim of this thesis is to create a module of recommender system. The work includes analysis of recommendation systems and the methods used in these systems, including a description of the calculations. This work also solves the cold start problem, which is a problem when generation of some good recommendations for the new user is needed, but the recommendation system has no or little information about this user. Based on analysis is in this thesis designed module for recommender system, which is applicable e.g. internet for e-commerce or other internet-based application. Part of this module is the realization of a platform Apache Mahout, which some parts are built on a distributed computing platform Apache Hadoop project. Furthermore, in this work, on the aforementioned platform Mahout, selected methods of calculating the similarity using selected criteria (e.g. the average time for a recommendation, and the number of users for who have not been able to generate recommendations) are tested.

Autism spectrum disorder (ASD) is a long-standing mental condition characterized by hindered mental growth and development. In 2018, 168 out of 10,000 children are said to be affected with Autism in the USA. As these children move to adulthood, they have difficulty in communicating with others, expressing themselves, maintaining eye contact, developing a well-functioning motor skill or sensory sensitivity, and paying attention for longer period. Some of these abnormalities, however, can be gradually improved if they are treated appropriately during their adulthood. Studies have shown that people with ASD can enhance their social-interactive skills by playing video games. During the past decades, however, educational games have been primarily developed for autistic children, but not for autistic adults. We have developed a gaming and recommendation system that suggests therapeutic games to autistic adults which can improve their social-interactive skills. The gaming system maintains the entertainment value of the games, to make sure people are interested in playing them, whereas the recommendation system suggests appropriate games for autistic adults to play. Customizable games are designed and implemented in Minecraft such that each game focuses on enhancing different weakness areas in autistic adults based on games that the users have not explored in the past. The effectiveness of the gaming and recommendation system is backed up by an empirical study which shows that recommending therapeutic games can aid in the improvement of social-interactive skills of adults with ASD so that they can live a better life in the years to come.

This thesis report describes an attempt to build a recommender system for recommending sporting goods in an e-commerce setting, using the customer purchase history as the input dataset. Two input datasets were considered, the item purchases dataset and the item-category dataset. Both the datasets are implicit, that is not explicitly rated by the customer. The data is also very sparse that very few users have purchased more than a handful of the items featured in the dataset. The report describes a method for dealing with both the implicit datasets as well as addressing the problem of sparsity. The report introduces SVD (Single Value Decomposition) with matrix factorization as a implementation for recommendation systems. Specifically implementations in the Apache Mahout machine learning framework. / Denna rapport beskriver ett tillvägagångssätt för att med kundernas köphistorik bygga ett rekommendationssystem för rekommendation av sportprodukter på en e-handelsplats. Två olika datamängder behandlas, köphistorik per produkt och kund, samt köpfrekvensen per produktkategori per kund i köphistoriken. Båda är implicita datamängder, vilket betyder att kunderna inte har explicit uttryckt en åsikt för eller emot produkten, utan implicit uttrycker preferens genom sitt köp. Datan är även mycket gles, vilket betyder att den enskilda kunden generellt bara köpt en liten del av den totala mängden av sålda varor. Rapporten behandlar en metod som behandlar både den implicita karaktären av data och gleshets problemet. Rapporten introducerar SVD (Single Value Decomposition) med matrisfaktorisering som en metod för att implementera rekommendationssystem. Specifikt implementerat med hjälp av maskininlärningsbiblioteket Apache Mahout.

Recommendation Systems or Recommender Systems have become widely popular due to surge of information at present time and consumer centric environment. Researchers have looked into a wide range of recommendation systems leveraging a wide range of algorithms. This study investigates three popular recommendation systems in existence, Collaborative Filtering, Content-Based Filtering, and Hybrid recommendation system. The famous MovieLens dataset was utilized for the purpose of this study. The evaluation looked into both quantitative and qualitative aspects of the recommendation systems. We found that from both the perspectives, the hybrid recommendation system performs comparatively better than standalone Collaborative Filtering or Content-Based Filtering recommendation system

In this project, we design a search query optimization system based on the user’s relevance feedback by generating customized query strings for existing patent alerts. Firstly, the Rocchio algorithm is used to generate a search string by analyzing the characteristics of related patents and unrelated patents. Then the collaborative filtering recommendation algorithm is used to rank the query results, which considering the previous relevance feedback and patent features, instead of only considering the similarity between query and patents as the traditional method. In order to further explore the performance of the optimization system, we design and conduct a series of evaluation experiments regarding TF-IDF as a baseline method. Experiments show that, with the use of generated search strings, the proportion of unrelated patents in search results is significantly reduced over time. In 4 months, the precision of the retrieved results is optimized from 53.5% to 72%. What’s more, the rank performance of the method we proposed is better than the baseline method. In terms of precision, top10 of recommendation algorithm is about 5 percentage points higher than the baseline method, and top20 is about 7.5% higher. It can be concluded that the approach we proposed can effectively optimize patent search results by learning relevance feedback.

Big data is an area focused on storing, processing and visualizing huge amount of data. Today data is growing faster than ever before. We need to find the right tools and applications and build an environment that can help us to obtain valuable insights from the data. Retail is one of the domains that collects huge amount of transaction data everyday. Retailers need to understand their customer’s purchasing pattern and behavior in order to take better business decisions. Market basket analysis is a field in data mining, that is focused on discovering patterns in retail’s transaction data. Our goal is to find tools and applications that can be used by retailers to quickly understand their data and take better business decisions. Due to the amount and complexity of data, it is not possible to do such activities manually. We witness that trends change very quickly and retailers want to be quick in adapting the change and taking actions. This needs automation of processes and using algorithms that are efficient and fast. In our work, we mine transaction data by modeling the data as graphs. We use clustering algorithms to discover communities (clusters) in the data and then use the clusters for building a recommendation system that can recommend products to customers based on their buying behavior.