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Before making a purchase, more and more consumers in recent years are consulting other consumers¡¦ product reviews online, to assist them in making a purchasing decision. However, due to the massive amount of online reviews, consumers can hardly get useful information effectively. Hence, information overload has become a problem. Query functions in search engines like Yahoo and Google can help users find some of the reviews that they need for specific information. Nevertheless, the returned pages from these search engines are still beyond the visual capacity of humans. Therefore, this study aims to develop a new concept of personalized recommendation based on consumer product reviews to solve the afore-mentioned problem. A series of laboratory experiment examines the effectiveness of the proposed approach and compares this approach with other traditional approaches on precision of recommendation. Meanwhile, the meaning of the recommendation behind each approach is explained. Lastly, the prototype of recommendation system based on the proposed approach is illustrated. Our system can display the trend of the gathered consumer reviews in a graphical way, such as a product satisfaction run chart. The development of recommendation systems is not only beneficial to consumers, but also advantageous to sellers.

Because electronic commerce has been flourishing in recent year, the amount and the variety of information on the web have also been rapidly increasing. However, many problems occur as the result of information overload. This thesis is to study the issue of information overload in the field of multimedia that covers not only medium of diffuse knowledge but also entertainment of everyday life. The main goal of this work is to use personalized recommendation technologies to help users select multimedia he is interested in. The thesis investigates two types of personalized recommendation: tag-based recommendation and context-aware recommendation. Regarding the former kind of recommendation, Folksonomy is the popular Web2.0 application that allows users tagging items to indicate the corresponding characteristics. These tags, provided by the users, directly or indirectly reflect his personal interests. Therefore the recommendation performance is enhanced when the tags are used with computational methods. On the other hand, the latter kind focuses on the contents and the relevant situations, because what multimedia is considered suitable for users can be different under different situations. The advantages of the personalized recommendation technology can improve performance of recommendation and take the context into account at the same time. Meanwhile this study also implements a working system for personalized multimedia recommendation.

Web-based Application Programming Interface (API) has become an important tool for modern software development. Many enterprises have developed various types of web APIs to support their business services, such as Google Map APIs, Twitter APIs, and eBay APIs. Due to the huge number of web APIs available in public domain, unfortunately, choosing relevant and low-risk web APIs has become an important problem for developers. This research is aimed at enhancing the recom- mendation engine for web APIs from several aspects. First, a new scanning technique is developed to detect the usage of web APIs in source codes. Using our scanning technique, we scanned over 1.7 million Open Source projects to capture the API usage patterns. Second, we integrated three machine learning models to predict compliance risks from web APIs based on their terms of services or other legal documents. Third, utilizing the knowledge learned from scanning results and compliance risks, we built a new recommendation engine for web APIs. We conducted an experimental study to evaluate our Web API recommendation engine and demonstrate its effectiveness. Some other modules, such as finding similar web APIs and searching function-related web APIs, have also been discussed. / Graduate

The purpose of this project is to create a website with a React-based prototype recommendation system of a large cultural collection. The aim of the website is to provide a function that allows a user to upload an image to which the system consequently recommends correlating artwork from the publicly available collection of the Metropolitan Museum of Modern Art (MET). The correlation coefficient between the uploaded image and the artworks from (MET) is acquired through Pearson Correlation. Furthermore the artwork with the highest correlation to the uploaded picture is shown first, then each subsequent artwork is shown in order of highest correlation. The main challenge for building this prototype was to combine the different components together with JavaScript and the REACT framework. The recommendation engine demands numerical representations of these artworks, and most effort was given to the automatic conversion of photos of artworks into a proper format for the recommendation engine.

People’s selection of leisure activities is a complex choice because of implicit human factors and explicit environmental factors. Satisfactory participation in leisure activities is an important task since keeping a regular active lifestyle can help to maintain and improve the wellbeing of people. Technology could help in selecting the most appropriate activities by designing and implementing activities, collecting people profiles and their preferences relations. In fact, recommendation systems, have been successfully used in the last years in similar tasks with different types of recommendation systems. This thesis aims at the design, implementation, and evaluation of recommendation systems that could help us to better understand the complex choice of selecting leisure activities. In this work, we first define an evaluation framework for different recommendations systems. Then we compare their performances using different evaluation metrics. Thus, we explore and try to better understand the user’s preferences over leisure activities. After, having a comprehensive analysis of modelling recommended items and leisure activities, we also design and implement a content-based leisure activity recommendation system to make use of a taxonomy of activities. Moreover, in the course of our research, we have collected and evaluated two datasets obtained one from the Meetup social network and the other from crowd-workers and made them available as open data sources for further evaluation in the recommendation system research community.

I takt med att Internet blir mer och mer tillgängligt och att informationsmängden på Internetkonstant ökar, har ett behov för rekommendationssystem uppkommit. Ett problem på internet äratt veta vem och vad man kan lita på. Ett sätt att komma runt det här tillitsproblemet är attanvända sig av social media. Samtidigt har sociala medier ständigt ökat i populäritet de senasteåren. Syftet med den här uppsatsen är att undersöka hur rekommendationssystem och socialamedier kan dra nytta av varandra samtidigt som ett praktiskt problem om att fårekommendationer från sina (online) vänner löses. Detta uppnås genom att forskningsstrategindesign science används och en IT-artefakt utvecklas. IT-artefakten är en prototyp av en ny etjänst.Utifrån en enkätundersökning på prototypen och grundidén, dras slutsaser om hur detgår att skapa ett generellt personligt rekommendationssystem. Forskningen visar att det går attskapa ett sådant system och att det finns ett behov av ett generellt personligtrekommendationssystem med utgångspunkt i den information som finns lagrad i sociala medier. / As the Internet becomes more and more available, and that the amount of information on theInternet is constantly increasing, a need for recommendation systems has emerged. A problemon the Internet is knowing who and what you can trust. One way to get around this trust issue isto use social media. Meanwhile, social media have consistently increased in popularity in recentyears. The purpose of this paper is to examine how recommendation systems and SNS canbenefit from each other while the practical problem of getting recommendations from one’s(online) friends are solved. This is achieved by using the research strategy design science anddeveloping an IT artifact. The IT artifact is a prototype of a new online service. Based on asurvey of the prototype and the main concept, conclusions are drawn about how a generalpersonal recommendation system can be created. The research shows that it is possible tocreate such a system and that there is a need for a general personal recommendation systembased on the information stored in social media. This essay is written in Swedish.

The new social media sites - blogs, micro-blogs, and social networking sites, among others - are gaining considerable momentum to facilitate collaboration and social interactions in general. These sites provide a tremendous asset for understanding social phenomena by providing a wide availability of novel data sources. Recent estimates suggest that social media sites are responsible for as much as one third of new Web content, in the forms of social networks, comments, trackbacks, advertisements, tags, etc. One critical and immediate challenge facing the MIS researchers then becomes - how to effectively utilize this huge wealth of social media data, to facilitate business knowledge discovery and decision making.Among these available data sources, social networks constitute the backbone of almost all social media sites. These network structures provide a rich description of the social scenes and contexts, which is helpful for us to address the above challenge. In this dissertation, I have primarily employed the probabilistic network models, to study various social network related problems arose from the use of social media services. In Chapter 2 and Chapter 3, I studied how information overload can affect the efficiency of information diffusion in online social networks (Delicious.com and Digg.com). Novel diffusion model were proposed to model the observed information overload. The models and their extensions are thoroughly evaluated by solving the Influence Maximization problem related to information diffusion and viral marketing applications. In Chapter 4, I studied the information overload in a micro-blogging application (Twitter.com) using a design science methodology. A content recommendation framework was proposed to help micro-blogging users to efficiently identify quality emergency news feeds. Chapter 5 presents a novel burst detection algorithm concerning identifying and analyzing correlated burst patterns by considering multiple inputs (data streams) that co-evolve over time. The algorithm was later used for discovering burst keywords/tag pairs from online social communities, which are strong indicators of emerging or changing user interests.Chapter 6 concludes this dissertation by highlighting major research contributions and future directions.

This dissertation studies the factors that negatively impact the accuracy of the collaborative filtering recommendation systems based on nonnegative matrix factorization (NMF). The keystone in the recommendation system is the rating that expresses the user's opinion about an item. One of the most significant issues in the recommendation systems is the lack of ratings. This issue is called "cold-start" issue, which appears clearly with New-Users who did not rate any item and New-Items, which did not receive any rating. The traditional recommendation systems assume that users are independent and identically distributed and ignore the connections among users whereas the recommendation actually is a social activity. This dissertation aims to enhance NMF-based recommendation systems by utilizing the imputation method and limiting the errors that are introduced in the system. External information such as trust network and item categories are incorporated into NMF-based recommendation systems through the imputation. The proposed approaches impute various subsets of the missing ratings. The subsets are defined based on the total number of the ratings of the user or item before the imputation, such as impute the missing ratings of New-Users, New-Items, or cold-start users or items that suffer from the lack of the ratings. In addition, several factors are analyzed that affect the prediction accuracy when the imputation method is utilized with NMF-based recommendation systems. These factors include the total number of the ratings of the user or item before the imputation, the total number of imputed ratings for each user and item, the average of imputed rating values, and the value of imputed rating values. In addition, several strategies are applied to select the subset of missing ratings for the imputation that lead to increasing the prediction accuracy and limiting the imputation error. Moreover, a comparison is conducted with some popular methods that are in common with the proposed method in utilizing the imputation to handle the lack of ratings, but they differ in the source of the imputed ratings. Experiments on different large-size datasets are conducted to examine the proposed approaches and analyze the effects of the imputation on accuracy. Users and items are divided into three groups based on the total number of the ratings before the imputation is applied and their recommendation accuracy is calculated. The results show that the imputation enhances the recommendation system by capacitating the system to recommend items to New-Users, introduce New-Items to users, and increase the accuracy of the cold-start users and items. However, the analyzed factors play important roles in the recommendation accuracy and limit the error that is introduced from the imputation.

The main aim of the course advising system is to build a course recommendation path for students to help them plan courses to successfully graduate on time. The recommendation path displays the list of courses a student can take in each quarter from the first quarter after admission until the graduation quarter. The courses are filtered as per the student’s interest obtained from a questionnaire asked to the student. The business logic involves building the recommendation algorithm. Also, the application is functionality-tested end-to-end by using nightwatch.js which is built on top of node.js. Test cases are written for every module and implemented while building the application.