There is a critical issue in explainable recommender systems that compounds the challenges of explainability yet is rarely tackled: the lack of ground-truth explanation texts for training. It is unrealistic to expect every user-item pair in a dataset to have a corresponding target explanation. Hence, we pioneer the first non-supervised explainability architecture for review-based collaborative filtering (called NEAR) as our novel contribution to the theory of explanation construction in recommender systems. While maintaining excellent recommendation performance, our approach reformulates explainability as a non-supervised (i.e., unsupervised and self-supervised) explanation generation task. We formally define two explanation types, both of which NEAR can produce. An invariant explanation, fixed for all users, is based on the unsupervised extractive summary of an item's reviews via embedding clustering. Meanwhile, a variant explanation, personalized for a specific user, is a sentence-level text generated by our customized Transformer conditioned on every user-item-rating tuple and artificial ground-truth (self-supervised label) from one of the invariant explanation's sentences. Our empirical evaluation illustrates that NEAR's rating prediction accuracy is better than the other state-of-the-art baselines. Moreover, experiments and assessments show that NEAR-generated variant explanations are more personalized and distinct than those from other Transformer-based models, and our invariant explanations are preferred over those from other contemporary models in real life.

The influence of Deep Learning on image identification and natural language processing has attracted enormous attention globally. The convolution neural network that can learn without prior extraction of features fits well in response to the rapid iteration of Android malware. The traditional solution for detecting Android malware requires continuous learning through pre-extracted features to maintain high performance of identifying the malware. In order to reduce the manpower of feature engineering prior to the condition of not to extract pre-selected features, we have developed a coloR-inspired convolutional neuRal networks (CNN)-based AndroiD malware Detection (R2-D2) system. The system can convert the bytecode of classes.dex from Android archive file to rgb color code and store it as a color image with fixed size. The color image is input to the convolutional neural network for automatic feature extraction and training. The data was collected from Jan. 2017 to Aug 2017. During the period of time, we have collected approximately 2 million of benign and malicious Android apps for our experiments with the help from our research partner Leopard Mobile Inc. Our experiment results demonstrate that the proposed system has accurate security analysis on contracts. Furthermore, we keep our research results and experiment materials on http://R2D2.TWMAN.ORG.

With the rapidly growing of real-time social media, like Twitter, many users share and discuss their interest topics through such platforms. Hashtag is a type of metadata tag which allows users to annotate their topics of tweets. For research usage, for example, hashtags can help the performance of event detection by observing the trend of hashtags. Although Twitter grows rapidly, hashtag growth is not as expected. Our dataset shows that there are less than 20% of all tweets containing hashtags. We think that it is caused by that most users may have no idea what hashtags are suitable for tweets they post. If we can recommend suitable hashtags to users, it can be one of the solutions to solve the problem of low usage rate of hashtag. Hashtag recommendation belongs to supervised learning problem. More labeled data for training the learning model can get higher performance in prediction. However, labeled data in hashtag recommendation is not so much due to low usage rate of hashtag. Thus, we want to exploit unlabeled data, i.e. non-hashtag tweets, to solve this problem. Now we have large amount of unlabeled data, but directly adding all non-hashtag tweets may not be helpful to train the model. To overcome this issue, we apply the weight-updating mechanisms to filter out the useless parts of non-hashtag tweets. These mechanisms also have to consider the temporal characteristics of hashtag due to the real-time nature of Twitter. The experimental results in this research show that adding non-hashtag tweets to extend original training data outperforms baseline methods which only exploit labeled data to train the model.

The Community Question Answer (CQA) service is a typical forum of Web 2.0 that shares knowledge among people. There are thousands of questions that are posted and solved every day. Because of the various users of the CQA service, question search and ranking are the most important topics of research in the CQA portal. In this study, we addressed the problem of identifying questions as being hard or easy by means of a probability model. In addition, we observed the phenomenon called knowledge gap that is related to the habit of users and used a knowledge gap diagram to illustrate how much of a knowledge gap exists in different categories. To this end, we proposed an approach called the knowledge-gap-based difficulty rank (KG-DRank) algorithm, which combines the user-user network and the architecture of the CQA service to find hard questions. We used f-measure, AUC, MAP, NDCG, precision@Top5 and concordance analysis to evaluate the experimental results. Our results show that our approach leads to better performance than other baseline approaches across all evaluation metrics.

Top-k queries have been studied mainly from the perspective of the user. Many researchers have focused on improving the efficiency of top-k problems. However, few studies have focused on the essential factors required for manufacturers to assess the potential market. A novel query type, namely, the reverse top-k, is used to assess the potential market and help manufacturers calculate the impact of their products. Given a potential product, reverse top-k will find the user preferences for which this product is in the top-k query result set. Although several algorithms can solve the reverse top-k problem, none that are available can solve the reverse top-k problem when the number of products or users is large. In this paper, we formally define our algorithm as FSP (filtering and space partition) and explain how FSP solves the reverse top-k problem. The main idea of FSP is to use the partition of the candidate space to reduce the searching of space for products. In our experimental results, FSP can find the same results as other algorithms, but FSP reduces the time cost from 231 msec to 32 msec.

In search advertising, advertisers should carefully compose keywords in order to enhance the opportunity for ads to be clicked. Thus, timely presenting proper advertisements to users will encourage them to click on search ads. Until now, how to efficiently improve the ad performance to earn more clicks remains a main task. In this paper, we focus on the scope of smart phone and produce a social intentional model with advertising based features to forecast future trend on ads' click-through rate (CTR). In terms of social intentional model, we analyze Chinese text content of technology forum to derive social intentional factors which are Hotness, Sentiment, Promotion, and Event. Our results indicate that with knowing public opinions or occurring events beforehand can efficiently enhance click prediction. This will be very helpful for advertisers on adjusting bidding keywords to improve ad performance via social intention.

Nowadays, the advance of social media has led to the explosive growth of opinion data. Therefore, sentiment analysis has attracted a lot of attentions. Currently, sentiment analysis applications are divided into two main approaches, the lexicon-based approach and the machine-learning approach. However, both of them face the challenge of obtaining a large amount of human-labeled training data and corpus. For the lexicon-based approach, it requires a sentiment lexicon to determine the opinion polarity. There are many existing benchmark sentiment lexicons, but they cannot cover all the domain-specific words meanings. Thus, automatic generation of a domain-specific sentiment lexicon becomes an important task. We propose a framework to automatically generate sentiment lexicon. First, we determine the semantic similarity between two words in the entire unlabeled corpus. We treat the words as nodes and similarities as weighted edges to construct word graphs. A graph-based semi-supervised label propagation method finally assigns the polarity to unlabeled words through the proposed propagation process. Experiments conducted on the microblog data, Twitter, show that our approach leads to a better performance than baseline approaches and general-purpose sentiment dictionaries.

Nowadays, people receive information of the news stories not only from newspapers but also from online news websites. They search important news stories in order to know what happen today. However, it is hard to browse all the news stories published on a day. It is necessary to identify which news stories are more newsworthy on the specific day. In this paper, we investigate how to automatically identify the importance of news stories for different news categories on a specific day by utilizing the influence propagation among communities and news categories. In particular, we build an influence propagation model which consists of three features: category relevance, bloggers' attention and bursty influence. Based on this influence propagation model, we propose a Cross-Category Social Influence Propagation (C-SIP) approach for scoring the importance of news stories on a specific day. We evaluate our approach by using the judgment of Story Ranking Task in TREC 2010 Blog Track. The experiment shows our approach attains a prominent performance in the retrieval of important news stories and gets 9.94% improvement over the best performance of participating systems in TREC 2010 Blog Track.

Folksonomy systems provide a way for users to share and organize bookmarks. The social relationship among users has become stronger with the rapid development of new technologies. Finding the leading objects has become an important topic. These research topics are always centered around finding the most popular pages or experts. In this paper, we propose a new notion of expertise, which we call user insight. User insight denotes the user's expertise in finding Web pages that are useful or have the potential to be popular pages before other users find them. To address the issue, we refer to three major types of Web pages, namely, isolated, well-known, and burgeoning. Burgeoning pages are exceptionally useful and attractive for users in a folksonomy system. In our paper, we build a time-based algorithm to estimate user insight. In addition, we discuss the social relationship within fan networks, and we propose a link-based algorithm called CAIS (Community-based Annotation Insight Search) to realize the reinforcement between users, communities and pages. Finally, we design several experiments to evaluate the performance of CAIS and compare it to other approaches. We prove that CAIS has a better performance for the user ranking of simulated data and real data from Delicious.

In the past, there have been many documents focusing on English reviews for sentiment analysis. These contain abundant research results which extract features and opinions, identify semantic orientation, and associate features with opinions. Although this approach has performed well for English reviews, it is not as successful with Chinese reviews. In this paper, we aim to develop a sentiment analysis system that is suitable for Chinese reviews. This system would extract features that users are interested in and detect those opinions with semantic orientations that accord with the dependency of certain features and opinions in one specific category. We then present users with the integrated results. Our experiments show that the derived system can effectively measure the dependency between features and opinions. The prominent performance of review sentiment analysis also validates the applicability of the proposed method.

More and More people use Cascading Style Sheets (CSS) to manage their Web pages, because CSS is easy and convenient to typesetting. However, CSS makes a Web page displayed in an ambiguous structure. The data extraction systems that based on mining the Web page structure would generate false judgments for these CSS-rich pages. For solving this issue, we propose a system that applies properties of CSS Web pages to extract data blocks. In this system, Web pages are converted into a visual tree and the entropy attributes of each node in a visual tree is calculated. In the experiment, the result shows the node attributes and the visual tree are useful to extract blocks on CSS Web pages. Our system also outperforms with other systems on container block extraction.

In recent years, search engines have already played the key roles among Web applications, and link analysis algorithms are the major methods to measure the important values of Web pages. These algorithms employ the conventional flat Web graph built by Web pages and link relations of Web pages to obtain the relative importance of Web objects. Previous researches have observed that PageRank-like link analysis algorithms have a bias against newly created Web pages. A new ranking algorithm called Page Quality was then proposed to solve this issue. Page Quality predicates future ranking values by the difference rate between the current ranking value and the previous ranking value. In this paper, we propose a new algorithm called DRank to diminish the bias of PageRank-like link analysis algorithms, and attain the better performance than Page Quality. In this algorithm, we model Web graph as a three-layer graph which includes Host Graph, Directory Graph and Page Graph by using the hierarchical structure of URLs and the structure of link relation of Web pages. We calculate the importance of Hosts, Directories and Pages by weighted graph we built and then the clustering distribution of PageRank values of pages within directories is observed. We can then predicate the more accurate values of page importance to diminish the bias of newly created pages by the clustering characteristic of PageRank. Experiment results show that DRank algorithm works well on predicating future ranking values of pages and outperform Page Quality.

With the fast development of Internet, the Web has already been an enormous database so far, which contains extremely abundant information. Most of Web pages are represented their content by using a list of objects, such as search engine results, product information of shopping Web sites and so on, and these objects form the primary information of each page. In this paper, we focus on the issues of mining primary information and the constituted object groups. The system is divided into three major phases: (1) By transforming each Web page into corresponding tree structures, our system can visit all regions of the Web page in an efficient way, and detects the informative parts. (2) We design and quantize several novel features according to the characters of regions of a Web page. (3) A weighting model is proposed that calculates the important degree of each region, we then extract the primary information of the Web pages. The experimental result proves our system can be applied to a large number of Web pages with different themes and styles to find the correct primary information and the list of corresponding objects

To increase the commercial value and accessibility of pages, most content sites tend to publish their pages with intrasite redundant information, such as navigation panels, advertisements, and copyright announcements. Such redundant information increases the index size of general search engines and causes page topics to drift. In this paper, we study the problem of mining intrapage informative structure in news Web sites in order to find and eliminate redundant information. Note that intrapage informative structure is a subset of the original Web page and is composed of a set of fine-grained and informative blocks. The intrapage informative structures of pages in a news Web site contain only anchors linking to news pages or bodies of news articles. We propose an intrapage informative structure mining system called WISDOM (Web intrapage informative structure mining based on the document object model) which applies Information Theory to DOM tree knowledge in order to build the structure. WISDOM splits a DOM tree into many small subtrees and applies a top-down informative block searching algorithm to select a set of candidate informative blocks. The structure is built by expanding the set using proposed merging methods. Experiments on several real news Web sites show high precision and recall rates which validates WISDOM'S practical applicability.

Due to the growth of dynamic page generation techniques, the amount and the complexity of Web pages has been increasing explosively, as has the information contained within Web pages. Redundant and irrelevant information is distributed and mixed throughout a page, making it difficult to automatically identify the useful information in that page. Consequently, we propose an information hierarchy in this paper, and, from that hierarchy, we can extract the significance and the relationship value of information contained within a Web page. We can then use this hierarchical structure to create a new browsing process. Our DOM-based Information Space Adsorption (DOMISA) system applies information theory to map information in a page into an information space, and our gradient tree adsorption (GTA) process uses the document object model (DOM) trees of pages to build information hierarchies. Experiments on several commercial news Web sites show high precision and recall rates achieved by DOMISA in determining information clusters of pages which validates its practical applicability to Web sites.

We study the problem of mining the informative structure of a news Web site that consists of thousands of hyperlinked documents. We define the informative structure of a news Web site as a set of index pages (or referred to as TOC, i.e., table of contents, pages) and a set of article pages linked by these TOC pages. Based on the Hyperlink Induced Topics Search (HITS) algorithm, we propose an entropy-based analysis (LAMIS) mechanism for analyzing the entropy of anchor texts and links to eliminate the redundancy of the hyperlinked structure so that the complex structure of a Web site can be distilled. However, to increase the value and the accessibility of pages, most of the content sites tend to publish their pages with intrasite redundant information, such as navigation panels, advertisements, copy announcements, etc. To further eliminate such redundancy, we propose another mechanism, called InfoDiscoverer, which applies the distilled structure to identify sets of article pages. InfoDiscoverer also employs the entropy information to analyze the information measures of article sets and to extract informative content blocks from these sets. Our result is useful for search engines, information agents, and crawlers to index, extract, and navigate significant information from a Web site. Experiments on several real news Web sites show that the precision and the recall of our approaches are much superior to those obtained by conventional methods in mining the informative structures of news Web sites. On the average, the augmented LAMIS leads to prominent performance improvement and increases the precision by a factor ranging from 122 to 257 percent when the desired recall falls between 0.5 and 1. In comparison with manual heuristics, the precision and the recall of InfoDiscoverer are greater than 0.956.

Benefiting from the growth of techniques of dynamic page generation, the amount and the complexity of Web pages increase explosively. The structures of Web pages which are dynamically generated by the same templates are thus similar to one another and are usually assembled by a set of fundamental information clusters These neighboring information clusters usually represent the similar semantics and form a larger cluster with the more generalized information. The hierarchical structure generated by information clusters in a bottom-up manner is called the information hierarchy of a page. We study the problem of mining the information hierarchies of pages in Web sites to recognize the information distribution of pages within the multilevel, multigranularity configurations. Explicitly, we propose an information clustering system that applies a top-down information centroid searching algorithm and a multigranularity centroid converging process on the document object model (DOM) trees of pages to build the information hierarchies of pages. Experiments on several real news Web sites show the high precision and recall rates of the proposed method on determining information clusters of pages and also validate its practical applicability to real Web sites.

In this paper, we study the problem of mining the informative structure of a news Web site which consists of thousands of hyperlinked documents. We define the informative structure of a news Web site as a set of index pages (or referred to as TOC, i.e., table of contents, pages) and a set of article pages linked by TOC pages through informative links. It is noted that the Hyperlink Induced Topics Search (HITS) algorithm has been employed to provide a solution to analyzing authorities and hubs of pages. However, most of the content sites tend to contain some extra hyperlinks, such as navigation panels, advertisements and banners, so as to increase the add-on values of their Web pages. Therefore, due to the structure induced by these extra hyperlinks, HITS is found to be insufficient to provide a good precision in solving the problem. To remedy this, we develop an algorithm to utilize entropy-based Link Analysis on Mining Web Informative Structures. This algorithm is referred to as LAMIS. The key idea of LAMIS is to utilize information entropy for representing the knowledge that corresponds to the amount of information in a link or a page in the link analysis. Experiments on several real news Web sites show that the precision and the recall of LAMIS are much superior to those obtained by heuristic methods and conventional ink analysis methods.