Topics for Bachelor or Master
Da ich im Bereich von Empfehlungssystemen und Semantic Web forsche, kann ich Dir ausgesprochen interessante Bachelor- oder Masterarbeiten im Bereich von Recommender Systems, Collaborative Filtering, Semantic Web, Social Networks und Data Mining anbieten. Dabei lege ich grossen Wert auf Innovation, d.h. die Themen umfassen meist neue und interessante Ideen und Wege. Aus diesem Grund möchte ich mögliche Themen an dieser Stelle nicht veröffentlichen.
Wenn Du interessiert bist an einem spannenden Projekt zu arbeiten oder selbst ein paar interessante Themen hast, können wir gerne über die entsprechenden Möglichkeiten sprechen.
Currently Advisored Students
Thomas Kaul: Collaborative Filtering-Based Poker Agent
Matthias Z'Brun: Game Theory: Effects of Trust Inferencing in Social Networks on Online Transaction Platforms
Advisored Master Students
, Online transaction platforms : implementation and multi-agent simulation of the effects of ratings and trust inferencing on online transaction platforms : a game theoretic perspective, 01 2011. (mastersthesis)
Online transaction platforms rely on the fair acting and behavior of the participants. It is a matter of two-sided markets, which needs both customers and suppliers to work. The transaction platform must provide the necessary trading conditions, that there is no abuse and does not cause migration of customers or suppliers. In the first part, this thesis investigates mechanisms that resist abuse provider strategies. For this purpose a game theory model of a transaction platform has been created. Measures (rating, trust, without protection) against abusive strategies were evaluated by using a simulation. The result of simulation shows that the application of the trust derived from the success of eliminating malicious behavior. On this basis, we have implemented a transaction platform TextKing which is already in use.
, Environs: visualization of recommendation clouds on the iPhone, 08 2010. (mastersthesis)
Recommender systems are a type of information retrieval and filtering systems that try to propose items to users according to their individual preferences. Collaborative Filtering is a method to implement such a recommender system through the prediction of ratings for items based on the social environment of the user. In a location recommender system the recommended items are locations, places or areas of interest. Commonly such location recommendations focus only on the current location of the user leaving out other important contextual factors such as time and the locations of other users. This thesis builds on the assumption that users might be interested in places or areas where other users with similar preferences currently are situated. We developed a visualization following the metaphor of a heatmap ? e.g. used of precipitation radar images ? where the locations of users are drawn on a map and shape clouds which recommend areas of interest visually. In addition, we develop an abstracted view of the cloud visualization called projection which recommends areas and places depending on hour, weekday and user preferences. We present our implementation of such a location recommender system, in particular the visualizations. Finally, we evaluate our visualization recommendation approach with a synthetic data set against other collaborative filtering algorithms and can present eligible results.
, OMORE - Private, Personal Movie Recommendations implemented in a Mozilla Firefox Add-on, August 2009. (mastersthesis)
Online stores and Web portals bring information about a myriad of items such as books, CDs, restaurants or movies at the user's fingertips. Although, the Web reduces the barrier to the information, the user is overwhelmed by the number of available items. Therefore, recommender systems aim to guide the user to relevant items. Current recommender systems store user ratings on the server side. This way the scope of the recommendations is limited to this server only. In addition, the user entrusts the operator of the server with valuable information about his preferences. In this thesis, we introduce our recommender system OMORE, a private, personal movie recommender, which learns the user model based on the user's movie ratings. To preserve privacy, OMORE is implemented as a Mozilla Firefox add-on, which stores the user's ratings and the learned user model locally at the client side. Although OMORE makes use of the movie features, which are provided by the different movie pages on the IMDb Web site, it is not restricted to IMDb only. The current implementation covers movie pages from Amazon.com, Blockbuster, Netflix and Rotten Tomatoes.
, spotting - Realisation and Analysis of a Location Recommender System Based on Facebook, December 2009. (diplomathesis)
Generating accurate recommendations for items, such as locations, movies or books, is challenging. Common Web-based recommender systems require information about the users? past to generate suitable recommendations for them. In this thesis we first present spotting.li, a location recommender system based on Facebook, which allows users to rate locations and generates recommendations inferred by their friends? ratings. In doing so, we examine requirments to successfully implement such a system using the latest web technologies (i.e., Grails) and describe key elements of our approach. Our focus is put on performance and providing an easy-to-use interface incorporating Google Maps. Furthermore, we analyse different recommendation approaches which leverage structural information from a social network to predict ratings. In particular, we examine the use of social network patterns, such as cliques and trendsetters, as well as direct friends and two levels of indirect friends. We finally conduct an extensive evaluation of these approaches, based on real data collected during the time of the thesis. To prove our findings, we test our dataset, based on 139 users, for statistical significance. We demonstrate that even a simple algorithm, such as the average rating, bares similar results to more elaborate algorithms.
Advisored Bachelor Students
, Building an agent for Texas Hold'em Poker based on a recommender system, 02 2011. (bachelorsthesis)
Poker provides an environment of great potential with well-defined rules for the research field of artificial intelligence. The popular card game provides incomplete information about the game state, non-deterministic outcome and stochastic elements where the outcome does not appear until thousands of hands have been played. These circumstances can be compared to making decisions in the real world and make the research interesting for other applications beyond poker. A major theme of this thesis is the development of an agent for Texas Hold?em Poker Sit and Go tournaments that plays skillful poker. For decision making, our approach is based on a recommender system. We mimic the behavior and strategies of a human poker player with an artificial intelligence agent. In various simulation setups we show that our approach is evaluated superior to simple poker opponents.
, Purple Leaf - Evaluation of the Adoption of New Features in a Web-Based Social Network, December 2008. (bachelorsthesis)
Purple Leaf is a social network which offers its member several possibilities to personalize its exclusive events by providing them unique online services. After the size of our platform suddenly increased from 300 initially invited guests to a multiple, we were obliged to completely revise the platform and enlarge our range of services. To embed these new services smoothly into the existing web presence, we fully restructured the application and changed the basis to a modern web framework. After that makeover, we designed five other services which we targeted to increase the customer loyalty and the entertainment value of our platform. Because new features are often not instantly accepted by existing users, we developed an integrated concept for boosting the acceptance of novel functionality. This concept is based on the technology acceptance model which was developed by Davis (1986). The model postulates that the actual use of a new feature is solely based on external factors. On the one hand, there are factors which influence the 'perceived ease-of-use' and on the other hand some that have impact on the 'perceived usefulness'. In order to foster the perceived ease-of-use, we developed several usability concepts and tried to figure out how Web 2.0 features can help to simplify different processes. Beside the creation of intuitive user interfaces and plain procedures, we worked on an elaborated data and application structure which itself also contributed a big part to the simplicity of the new functionality. After we had embedded the services into our Internet portal, we started to analyze the acceptance of one new feature: 'The most favored Guest'. This service allows every sign up member to define his personal list of favored guests for an upcoming event. Once the selected users are informed about their election, they, in turn, have the chance to define their own list. After a first round of selection, we tried to boost the personal acceptance of our members by providing specific incentives. Beside the active interventions into the process of adoption, we also analyzed a passive phenomenon: Does some kind of peer pressure exist within virtual cliques? If so, there might emerge some interesting changes in common marketing strategies which could narrow down the target audience to some single users of the network. In addition, we visualized some of the encountered situations and putted them together in an illustrated book as supplement to this paper.