Abstract The studies of browsing behavior have gained increasing attention in web analysis for providing better service. Most of the conventional approaches focus on simple indices such as average dwell time and conversion rate. These indices make similar evaluations to websites even if their features are significantly different. Moreover, such statistical indices are not sensitive to the dynamics of users’ interests. In this paper, we propose a new framework for measuring a website’s attractiveness that takes into account both the distribution and dynamics of users’ interests. Within the framework, we define a new index for the website, called Attractiveness Factor, which evaluates the degree of users’ attention. It consists of three procedures: First, we capture the transition of users’ interests during browsing by solving a nonnegative matrix factorization and constrained network flow problems. To accommodate multiple types of interests of a user, we applied a soft clustering as opposed to a hard clustering to model attributes of users and websites. Second, for each website, the feature of each cluster is obtained by fitting the dwell time distribution with Weibull distribution. Finally, we calculate Attractiveness Factor of a website by applying the results of clustering and fitting. Attractiveness Factor depends on the distribution of the dwell time of users interested in the website, which reflects the change of interest of users. Numerical experiments with real web access data of Yahoo Japan News are conducted by solving extremely large-scale optimization problems. They show that Attractiveness Factor captures more exceptional information about browsing behavior more effectively than well-used indices. Attractive factors give low ratings to category pages; however, it can assign high ratings to websites that attract many people, such as hot topic news about the 2018 FIFA World Cup, Japan’s new imperial era’ REIWA,’ and North Korea—the United States Hanoi Summit. Moreover, we demonstrate that Attractiveness Factor can detect the tendency of users’ attention to each website at a given time interval of the day.

It is necessary to understand the content of articles and user preferences to make effective news recommendations. While ID-based methods, such as collaborative filtering and low-rank factorization, are well known for making recommendations, they are not suitable for news recommendations because candidate articles expire quickly and are replaced with new ones within short spans of time. Word-based methods, which are often used in information retrieval settings, are good candidates in terms of system performance but have issues such as their ability to cope with synonyms and orthographical variants and define "queries" from users' historical activities. This paper proposes an embedding-based method to use distributed representations in a three step end-to-end manner: (i) start with distributed representations of articles based on a variant of a denoising autoencoder, (ii) generate user representations by using a recurrent neural network (RNN) with browsing histories as input sequences, and (iii) match and list articles for users based on inner-product operations by taking system performance into consideration. The proposed method performed well in an experimental offline evaluation using past access data on Yahoo! JAPAN's homepage. We implemented it on our actual news distribution system based on these experimental results and compared its online performance with a method that was conventionally incorporated into the system. As a result, the click-through rate (CTR) improved by 23% and the total duration improved by 10%, compared with the conventionally incorporated method. Services that incorporated the method we propose are already open to all users and provide recommendations to over ten million individual users per day who make billions of accesses per month.

Web searchers often use a Web search engine to find a way or means to achieve his/her goal. For example, a user intending to solve his/her sleeping problem, the query "sleeping pills" may be used. However, there may be another solution to achieve the same goal, such as "have a cup of hot milk" or "stroll before bedtime." The problem is that the user may not be aware that these solutions exist. Thus, he/she will probably choose to take a sleeping pill without considering these solutions. In this study, we define and tackle the alternative action mining problem. In particular, we attempt to develop a method for mining alternative actions for a given query. We define alternative actions as actions which share the same goal and define the alternative action mining problem as similar in the search result diversification. To tackle the problem, we propose leveraging a community Q&A (cQA) corpus for mining alternative actions. We propose a method to compute how well two actions can be alternative actions by using a question-answer structure in a cQA corpus. Our method builds a question-action bipartite graph and recursively computes how well two actions can be alternative actions. We conducted experiments to investigate the effectiveness of our method using two newly built test collections, each containing 50 queries. The experimental results indicated that our proposed method outperformed the query suggestion methods provided by the commercial search engines in terms of D#-nDCG.

Query ambiguity is a useful metric for search engines to understand users' intents. Existing methods quantify query ambiguity by calculating an entropy of clicks. These methods assign each click to a one-hot vector corresponding to some mutually exclusive groups. However, they cannot incorporate non-obvious structures such as similarity among documents. In this paper, we propose a new approach for quantifying query ambiguity using topic distributions. We show that it is a natural extension of an existing entropy-based method. Further, we use our approach to achieve topic-based extensions of major existing entropy-based methods. Through an evaluation using e-commerce search logs combined with human judgments, our approach successfully extended existing entropy-based methods and improved the quality of query ambiguity measurements.

Contextual advertising is a form of textual advertising usually displayed on third party Web pages. One of the main problems with contextual advertising is determining how to select ads that are relevant to the page content and/or the user information in order to achieve both effective advertising and a positive user experience. Typically, the relevance of an ad to page content is indicated by a tf-idf score that measures the word overlap between the page and the ad content, so this problem is transformed into a similarity search in a vector space. However, such an approach is not useful if the vocabulary used on the page is expected to be different from that in the ad. There have been studies proposing the use of semantic categories or hidden classes to overcome this problem. With these approaches it is necessary to expand the ad retrieval system or build new index to handle the categories or classes, and it is not always easy to maintain the number of categories and classes required for business needs. In this work, we propose a translation method that learns the mapping of the contextual information to the textual features of ads by using past click data. The contextual information includes the user's demographic information and behavioral information as well as page content information. The proposed method is able to retrieve more preferable ads while maintaining the sparsity of the inverted index and the performance of the ad retrieval system. In addition, it is easy to implement and there is no need to modify an existing ad retrieval system. We evaluated this approach offline on a data set based on logs from an ad network. Our method achieved better results than existing methods. We also applied our approach with a real ad serving system and compared the online performance using A/B testing. Our approach achieved an improvement over the existing production system.