Session: Emerging Themes
Date: Wednesday, September 12, 16:30-18:00
- An Approach to Context-Based Recommendation in Software Development
by Bruno Antunes, Joel Cordeiro and Paulo Gomes
A software developer programming in an object-oriented programming language deals with a source code structure that may contain hundreds of source code elements. These elements are commonly related to each other and working on a specific element may require the developer to access other related elements. We propose a recommendation approach that uses the context of the developer to retrieve and rank recommendations of relevant source code elements in the IDE. These recommendations provide a shortcut to reach the desired elements and increase the awareness of the developer in relation to elements that may be of interest in that moment. We have tested our approach with a group of developers and the results show that context has a promising role in predicting and ranking the source code elements needed by a developer at each moment.
- A Semantic Approach to Recommending Text Advertisements for Images
by Weinan Zhang, Li Tian, Xinruo Sun, Haofen Wang and Yong Yu
In recent years, more and more images have been uploaded and published on the Web. Along with text Web pages, images have been becoming important media to place relevant advertisements. Visual contextual advertising, a young research area, refers to finding relevant text advertisements for a target image without any textual information (e.g., tags). There are two existing approaches, advertisement search based on image annotation, and more recently, advertisement matching based on feature translation between images and texts. However, the state of the art fails to achieve satisfactory results due to the fact that recommended advertisements are syntactically matched but semantically mismatched. In this paper, we propose a semantic approach to improving the performance of visual contextual advertising. More specifically, we exploit a large high-quality image knowledge base (ImageNet) and a widely-used text knowledge base (Wikipedia) to build a bridge between target images and advertisements. The image-advertisement match is built by mapping images and advertisements into the respective knowledge bases and then finding semantic matches between the two knowledge bases. The experimental results show that semantic match outperforms syntactic match significantly using test images from Flickr. We also show that our approach gives a large improvement of 16.4% on the precision of the top 10 matches over previous work, with more semantically relevant advertisements recommended.
- Ads and the City: Considering Geographic Distance Goes a Long Way
by Diego Saez-Trumper, Daniele Quercia and Jon Crowcroft
Social-networking sites have started to offer tools that suggest “guests” who should be invited to user-defined social events (e.g., birthday parties, networking events). The problem of how to recommend people to events is similar to the more traditional (recommender system) problem of how to recommend events (items) to people (users). Yet, upon Foursquare data of “who visits what” in the city of London, we show that a state-of-the-art recommender system does not perform well -mainly because of data sparsity. To fix this problem, we add domain knowledge to the recommendation process. From the complex system literature in human mobility, we learn two insights: 1) there are special individuals (often called power users) who visit many places; and 2) individuals go to a venue not only because they like it but also because they are close-by. We model these insights into two simple models and learn that: 1) simply recommending power users works better than random but is far from producing the best recommendations; 2) an item-based recommender system produces accurate recommendations; and 3) recommending places that are closest to a user’s geographic center of interest produces recommendations that are as accurate as, if not more accurate than, item-based recommender’s. This last result has practical implications as it offers guidelines for designing location-based recommender systems and for partly addressing cold-start situations.
- BlurMe: Inferring and Obfuscating User Demographics Based on Ratings
by Smriti Bhagat, Udi Weinsberg, Stratis Ioannidis and Nina Taft
User demographics, such as age, gender and ethnicity, are routinely used for targeting content and advertising products to users. Similarly, recommender systems utilize user demographics for personalizing recommendations and overcoming the cold-start problem. Often, privacy-concerned users do not provide these details in their online profiles. In this work, we show that a recommender system can infer the gender of a user with high accuracy, based solely on the ratings provided by users (without additional metadata), and a relatively small number of users who share their demographics. Focusing on gender, we design techniques for effectively adding ratings to a user’s profile for obfuscating the user’s gender, while having an insignificant effect on the recommendations provided to that user.
RecSys 2012 (Dublin)
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