The abundance of information available on the internet necessitates means of quickly finding what is relevant for the individual user. To this end, there has been much research concerning recommender systems and lately specifically methods using deep learning for such systems. This work proposes a Multi-Armed Bandit as a recommender for suggested sites on a browser start page. The system is compared to a pre-existing baseline and does not manage to outperform it in the setting used in controlled experiments. A Neural Collaborative Filtering system is then constructed using a stacked autoencoder and is used to produce user preference vectors that are inserted in the bandit in the hope of improving its performance. Analysis indicates that the bandit solution works better as the number of items grows. The user-informed initialisation used in this work shows a trend of improving over a randomly-initialised bandit, but results are inconclusive. This work also contributes an analysis of the problem domain including which factors impact the performance on the model training for preference vectors, and the performance of the bandit algorithms.

Rekommendationssystem är idag väsentliga för att navigera den enorma mängd produkter tillgängliga via internet. Då social media i form av Twitter vid tidigare tillfällen använts för att generera filmrekommendationer har detta främst varit för att hantera cold-start, ett vanligt drabbande problem för collaborative-filtering. I detta arbete adresseras istället hur top-k rekommendationer påverkas vid integrering av social media data i rekommendationssystemet. För att svara på denna fråga har en prototyp av nytt slag utvecklats inom processmodellen för Design Science. Systemet rankar om top-k rekommendationer baserat på resultatet av social matchning där användares Tweets matchas med nyckelord för filmer genom latent semantic indexing (LSI) similarity. Prototypen evalueras genom experiment som adresserar funktionalitet, noggrannhet, konsekvens och prestanda. Resultatet visar att mätetalen NDCG och MAP för top-k rekommendationer förbättras med social matching jämfört med att enbart använda collaborative filtering. / Recommender systems are a crucial part of navigating the vast number of products on the internet. Social media, in the form of Twitter microblogs, has been previously used to produce movie recommendations, yet this has mainly been to solve cold-start, a common problem in collaborative filtering environments. This work addresses how top-k recommendations in a collaborative filtering environment are affected when augmented with social media data. To answer this question a novel prototype is developed following a design science process model. This system re-ranks top-k recommendations based on a social matching process where Tweets are matched with movie keywords through latent semantic indexing (LSI) similarity. The prototype is evaluated through experiments regarding functionality, accuracy, consistency, and performance. The results show that NDCG and MAP metrics of the top-k recommendations improve with social matching compared to only using the collaborative filtering algorithms.

Indiana University-Purdue University Indianapolis (IUPUI) / Recommender systems are widely used in many domains. In this work, the importance of a recommender system in an online learning platform is discussed. After explaining the concept of adding an intelligent agent to online education systems, some features of the Course Networking (CN) website are demonstrated. Finally, the relation between CN, the intelligent agent (Rumi), and the recommender system is presented. Along with the argument of three different approaches for building a community recommendation system. The result shows that the Neighboring Collaborative Filtering (NCF) outperforms both the transfer learning method and the Continuous bag-of-words approach. The NCF algorithm has a general format with two various implementations that can be used for other recommendations, such as course, skill, major, and book recommendations.

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This report targets a specific problem for recommender algorithms which is the new item problem and propose a method with sentiment analysis as the main tool. Collaborative filtering algorithms base their predictions on a database with users and their corresponding ratings to items. The new item problem occurs when a new item is introduced in the database because the item has no ratings. The item will therefore be unavailable as a recommendation for the users until it has gathered some ratings. Products that can be rated by users in the online community often has experts that get access to these products before its release date for the consumers, this can be taken advantage of in recommender systems. The experts can be used as initial guides for predictions. The method that is used in this report relies on sentiment analysis to translate written reviews by experts into a rating based on the sentiment of the text. This way when a new item is added it is also added with the ratings of experts in the field. The result from this study shows that the recommender algorithm slope one can generate more reliable recommendations with a group of expert users than without when a new item is added to the database. The expert users that is added must have ratings for other items as well as the ratings for the new item to get more accurate recommendations. / Denna rapport studerar påverkan av problemet med nya objekt i rekommendationsalgoritmen Slope One och en metod föreslås i rapporten för att lösa det specifika problemet. Problemet uppstår när ett nytt objekt läggs till i en databas då det inte finns några betyg som getts till objektet/produkten. Då rekommendationsalgoritmer som Slope One baserar sina rekommendationer på relationerna mellan användares betyg av filmer så blir träffsäkerheten låg för en rekommendation av en film med få betyg. Metoden som föreslås i rapporten involverar attitydanalys som det huvudsakliga verktyget för att få information som kan ersätta faktiska betyg som användare gett en produkt. När produkter kan bli betygsatta av användare på olika forum på internet så finns det ofta experter får tillgång till produkten innan den släpps till omvärlden, den information som dessa experter har kan användas för att fylla det informationsgap som finns när ett nytt objekt läggs till. Dessa experter kommer då initiellt att användas som guide för rekomendationssystemet. Så när ett nytt objekt läggs till så görs det tillsammans med betyg från experter för att få mer träffsäkra rekomendationer. Resultatet från denna studie visar att Slope One genererar mer träffsäkra rekommendationer då en ny produkt läggs till i databasen med ett antal betyg som genererats genom attitydanalysanalys på experters textrecensioner. Det är värt att notera att ett betyg enbart för dessa expertanvändare inte håller utan experterna måste ha betyg av andra produkter inom samma område för kunna influera rekommendationer för den nya produkten.

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Topic management is the task of gathering, evaluating, organizing, and sharing a set of web sites for a specific topic. Current web tools do not provide adequate support for this task. We created and continue to develop the TopicShop system to address this need. TopicShop includes (1) a web crawler/analyzer that discovers relevant web sites and builds site profiles, and (2) user interfaces for information workspaces. We conducted an empirical pilot study comparing user performance with TopicShop vs. Yahooï . Results from this study were used to improve the design of TopicShop. A number of key design changes were incorporated into a second version of TopicShop based on results and user comments of the pilot study including (1) the tasks of evaluation and organization are treated as integral instead of separable, (2) spatial organization is important to users and must be well supported in the interface, and (3) distinct user and global datasets help users deal with the large quantity of information available on the web. A full empirical study using the second iteration of TopicShop covered more areas of the World Wide Web and validated results from the pilot study. Across the two studies, TopicShop subjects found over 80% more high-quality sites (where quality was determined by independent expert judgements) while browsing only 81% as many sites and completing their task in 89% of the time. The site profile data that TopicShop provide -- in particular, the number of pages on a site and the number of other sites that link to it -- were the key to these results, as users exploited them to identify the most promising sites quickly and easily. We also evaluated a number of link- and content-based algorithms using a dataset of web documents rated for quality by human topic experts. Link-based metrics did a good job of picking out high-quality items. Precision at 5 (the common information retrieval metric indicating the percentage of high quality items selected that are actually high quality) is about 0.75, and precision at 10 is about 0.55; this is in a dataset where 32% of all documents were of high quality. Surprisingly, a simple content-based metric, which ranked documents by the total number of pages on their containing site, performed nearly as well. These studies give insight into users' needs for the task of topic management, and provide empirical evidence of the effectiveness of task-specific interfaces (such as TopicShop) for managing topical collections. / Ph. D.