Modeling user activities on the Web is a key problem for various Web services, such as news article recommendation and ad click prediction. In our work-in-progress paper[1], we introduced an approach that summarizes each sequence of user Web page visits using Paragraph Vector[3], considering users and URLs as paragraphs and words, respectively. The learned user representations are used among the user-related prediction tasks in common. In this paper, on the basis of analysis of our Web page visit data, we propose Backward PV-DM, which is a modified version of Paragraph Vector. We show experimental results on two ad-related data sets based on logs from Web services of Yahoo! JAPAN. Our proposed method achieved better results than those of existing vector models.

Web browsing services are expanding as smartphones are becoming increasingly popular worldwide. To provide customers with appropriate quality of web-browsing services, quality design and in-service quality management on the basis of quality of experience (QoE) is important. We propose a web-browsing QoE estimation model. The most important QoE factor for web-browsing is the waiting time for a web page to load. Next, the variation in the communication quality based on a mobile network should be considered. We developed a subjective quality assessment test to clarify QoE characteristics in terms of waiting time using 20 different types of web pages and constructed a web-page QoE estimation model. We then conducted a subjective quality assessment test of web-browsing to clarify the relationship between web-page QoE and web-browsing QoE for three web sites. We obtained the following two QoE characteristics. First, the main factor influencing web-browsing QoE is the average web-page QoE. Second, when web-page QoE variation occurs, a decrease in web-page QoE with a huge amplitude causes the web-browsing QoE to decrease. We used these characteristics in constructing our web-browsing QoE estimation model. The verification test results using non-training data indicate the accuracy of the model. We also show that our findings are applicable to web-browsing quality design and solving management issues on the basis of QoE.

Due to the depletion of the public IPv4 address pool, Internet service providers will not be able to supply their new customers with public IPv4 addresses in the near future. Either they give private IPv4 addresses and use carrier grade NAT (CGN) or they move towards IPv6 and provide NAT64 service to the IPv6 only clients who want to reach IPv4 only servers. In both cases they must use a stateful NAT/NAT64 solution. When dimensioning a NAT/NAT64 gateway, the port number consumption of the clients is a key factor as the port numbers are 16 bits long and a unique one has to be provided for every session (when using traditional type NAPT, which does not include the destination IP address and port number in the tuple for the identification of TCP sessions) and a single web client may use several hundred sessions and an equal number of port numbers according to literature. In this paper, we present a method for the estimation of the port number consumption of web browsing. The method is based on the port number consumption measurements of the most popular web sites and their combination using the number of the visitors of the web sites as weight factors. We propose the resulting curve as an approximation of a general profile of the average port number consumption of web browsers after the first click, but without taking into consideration the effect of the web users' browsing behavior. We also discuss the case of the extended NAPT, which can reuse the source port numbers towards different destination IP addresses and/or destination port numbers. We propose a formula and give measurement results for the extended NAPT gateways, too. We disclose the measurement method in detail and provide the measurement scripts in Linux, too.

Web browsing task is based on depth-first searching scheme, so that searching relevant information from Web may be very tedious. In this paper, we propose personal browsing assistant system based on user intentions modeling. Before explicitly requested by a user, this system can analyze the prefetched resources from the hyperlinked Webpages and compare them with the estimated user intention, so that it can help him to make a better decision like which Webpage should be requested next. More important problem is the semantic heterogeneity between Web spaces. It makes the understandability of locally annotated resources more difficult. We apply semantic annotation, which is a transcoding procedure with the global ontology. Therefore, each local metadata can be semantically enriched, and efficiently comparable. As testing bed of our experiment, we organized three different online clothes stores whose images are annotated by semantically heterogeneous metadata. We simulated virtual customers navigating these cyberspaces. According to the predefined preferences of customer models, they conducted comparison-shopping. We have shown the reasonability of supporting the Web browsing, and its performance was evaluated as measuring the total size of browsed hyperspace.