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No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

Diss. Univ. Bremen, 2007. / Computer Science (Springer-11645).

<p> The distribution of the amount of preference information across customers is not the same in every domain of recommendation problems. It is necessary to treat each user differently based on their available preference information. In this dissertation, we have proposed three novel recommender system approaches that depend on each user&rsquo;s preference information and can produce recommendations in a user-specific, parametric way. This parametric approach allows different weights to be assigned to different kinds of page-page similarity features used in the recommendation process, depending on the user group to which a particular user belongs. This novel approach of incorporation of different kinds of item-item (or page-page) similarities is shown to result in a significant increase in recommendation accuracy. In our first approach, we incorporated content-based and co-occurrence-based page-page similarities parametrically, by determining relative weights of the two component page-page similarities in user-specific way. We implemented a Map-Reduce based, parametric, hybrid recommendation system in order to solve the scalability issues. Experimental results showed better accuracy for this unique, scalable, and user-specific parametric approach, compared to that of another related work. In our second approach, we used clustering-based, hybrid recommendation system in user-specific way to get better accuracy and to further alleviate scalability issues by exploiting pre-computed clusters. This clustering-based incorporation approach showed better result than our first approach for users having extremely small amount of preference information. Finally, in our third approach, we proposed a graph-based, hybrid recommendation system. Two graphs using, respectively, content similarity and co-occurrence similarity were created. An approach involving features derived from these two graphs to make web page recommendations was introduced. For each user-page pair, one combined feature component was first obtained by making a weighted summation of the eight feature sets from each graph. Use of supervised learning for deriving feature weights to obtain combined feature components showed much more promising results, compared to the first two methods. Finally, the two feature components, from the two graphs were combined in user-specific way to train a model and make recommendations. To the best of our knowledge, ours is the first such effort in the recommender systems context.</p>

<p> Programmers, software testers, and cyber-security analysts have a need to understand the behaviors that their programs might exhibit. We consider a program's behaviors to be its actions manifesting some effect beyond its own internal state. A program generally exhibits such behaviors by making API calls. One particularly powerful strategy for gaining an understanding of a program's behaviors is to witness their exhibition as the program runs. However, in order to witness a program's behaviors, one must first be able to elicit the program into exhibiting them. In the present work, a method is presented that serves to automatically and efficiently elicit a program into exhibiting many or all of its potential behaviors. The method works by guiding concolic execution towards the control flow paths along which a program's behaviors are more likely to be exhibited. First, an upfront interprocedural data flow analysis is employed to compute how API call statements reach one another and various other program points with respect to the program's control flow. The resulting information is then used to guide the path selection of concolic execution so as to give preference to paths along which more API call statements can be reached. An evaluation of the presented method shows that it more efficiently elicits program behaviors than does usage of non-guided concolic execution. In particular, the percentage increase in API call statements executed using our behavior elicitation method as compared to a common non-guided strategy ranged from 2% up to 287%, with a median percentage gain of 69.74%. </p>