Makbule Gulcin Ozsoy

Text summarization using Latent Semantic Analysis

Text summarization solves the problem of presenting the information needed by a user in a compact form. There are different approaches to creating well-formed summaries. One of the newest methods is the Latent Semantic Analysis (LSA). In this paper, different LSA-based summarization algorithms are explained, two of which are proposed by the authors of this paper. The algorithms are evaluated on Turkish and English documents, and their performances are compared using their ROUGE scores. One of our algorithms produces the best scores and both algorithms perform equally well on Turkish and English document sets.

Result Diversification for Tweet Search

Being one of the most popular microblogging platforms, Twitter handles more than two billion queries per day. Given the users’ desire for fresh and novel content but their reluctance to submit long and descriptive queries, there is an inevitable need for generating diversified search results to cover different aspects of a query topic. In this paper, we address diversification of results in tweet search by adopting several methods from the text summarization and web search domains. We provide an exhaustive evaluation of all the methods using a standard dataset specifically tailored for this purpose. Our findings reveal that implicit diversification methods are more promising in the current setup, whereas explicit methods need to be augmented with a better representation of query sub-topics.

Trust based recommendation systems

It is difficult for the users to reach the most appropriate and reliable item for them among vast number of items and comments on these items. Recommendation systems and trust/reputation systems are one of the solutions to deal with this problem with the help of personalized services. These systems suggest items to the user by estimating the ratings that user would give to them. Use of trust data for giving recommendation has emerged as a new way for giving better recommendations. In the literature, it is shown that trust based recommendation approaches perform better than the ones that are only based on user similarity, or item similarity. In this paper, a comparative review of recommendation systems, trust/reputation systems, and their combined usage is presented. Then, a sample trust based agent oriented recommendation system is proposed and its effectiveness is justified with the help of some experiments.

Making recommendations by integrating information from multiple social networks

It is becoming a common practice to use recommendation systems to serve users of web-based platforms such as social networking platforms, review web-sites, and e-commerce web-sites. Each platform produces recommendations by capturing, maintaining and analyzing data related to its users and their behavior. However, people generally use different web-based platforms for different purposes. Thus, each platform captures its own data which may reflect certain aspects related to its users. Integrating data from multiple platforms may widen the perspective of the analysis and may help in modeling users more effectively. Motivated by this, we developed a recommendation framework which integrates data collected from multiple platforms. For this purpose, we collected and anonymized datasets which contain information from several social networking and social media platforms, namely BlogCatalog, Twitter, Flickr, Facebook, YouTube and LastFm. The collected and integrated data forms a consolidated repository that may become a valuable source for researchers and practitioners. We implemented a number of recommendation methodologies to observe their performance for various cases which involve using single versus multiple features from a single source versus multiple sources. The conducted experiments have shown that using multiple features from multiple sources is expected to produce a more concrete and wider perspective of user’s behavior and preferences. This leads to improved recommendation outcome.

Inference of gene regulatory networks via multiple data sources and a recommendation method

Gene regulatory networks (GRNs) are composed of biological components, including genes, proteins and metabolites, and their interactions. In general, computational methods are used to infer the connections among these components. However, computational methods should take into account the general features of the GRNs, which are sparseness, scale-free topology, modularity and structure of the inferred networks. In this work, observing the common aspects between recommendation systems and GRNs, we decided to map the GRNs inspiring problem into a recommendation problem and then used a known recommendation method to predict gene relationships based on multiple data sources, e.g., which molecules regulate others. The method we used is based on Pareto dominance and collaborative filtering. For the experiments, we used a combination of two datasets, namely microarray data and transcription factor (TF) binding data. The reported results show that using information from multiple sources improves the performance. Also, we observed that employing an approach from the recommendation systems domain revealed interesting results and good performance.

Modeling Individuals and Making Recommendations Using Multiple Social Networks

Web-based platforms, such as social networks, review web-sites, and e-commerce web-sites, commonly use recommendation systems to serve their users. The common practice is to have each platform captures and maintains data related to its own users. Later the data is analyzed to produce user specific recommendations. We argue that recommendations could be enriched by considering data consolidated from multiple sources instead of limiting the analysis to data captured from a single source. Integrating data from multiple sources is analogous to watching the behavior and preferences of each user on multiple platforms instead of a limited one platform based vision. Motivated by this, we developed a recommendation framework which utilizes user specific data collected from multiple platforms. To the best of our knowledge, this is the first work aiming to make recommendations by consulting multiple social networks to produce a rich modeling of user behavior. For this purpose, we collected and anonymized a specific dataset that contains information from BlogCatalog, Twitter and Flickr web-sites. We implemented several different types of recommendation methodologies to observe their performances while using single versus multiple features from a single source versus multiple sources. The conducted experiments showed that using multiple features from multiple social networks produces a wider perspective of user behavior and preferences leading to improved recommendation outcome.

Contrastive Max-Sum Opinion Summarization

People can reach all kinds of information online incuding reviews and comments on products, movies, holiday destinations and so on. However, one usually need to go through the reviews to have an objective opinion the positive and the negative aspects of the item reviewed. We aim to provie a method that will extract positive and negative opinions on a specific aspect and compare them in an attempt to ease on the information overflow. Contrastive opinion summarization (COS) aims to solve this issue. COS methods extract representative and comparative sentences in terms of specific aspects of a product. In this paper, we propose a new COS method, namely Contrastive Max-Sum Opinion Summarization (CMSOS). This method considers representativeness and contrastiveness at the same time. For the evaluation, we use an English dataset which was specifically created for COS studies. In addition, we created a new dataset in Turkish and shared it publicly. We provide the results on both datasets with our method.

Correction to: Realizing drug repositioning by adapting a recommendation system to handle the process

Following publication of the original article [1], the authors reported that there was an error in the spelling of the name of one of the authors.