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E-commerce systems experience poor quality of performance when the number of records in the customer database increases due to the gradual growth of customers and products. Applying implicit hidden features into the recommender system (RS) plays an important role in enhancing its performance due to the original dataset’s sparseness. In particular, we can comprehend the relationship between products and customers by analyzing the hierarchically expressed hidden implicit features of them. Furthermore, the effectiveness of rating prediction and system customization increases when the customer-added tag information is combined with hierarchically structured hidden implicit features. For these reasons, we concentrate on early grouping of comparable customers using the clustering technique as a first step, and then, we further enhance the efficacy of recommendations by obtaining implicit hidden features and combining them via customer’s tag information, which regularizes the deep-factorization procedure. The idea behind the proposed method was to cluster customers early via a customer rating matrix and deeply factorize a basic WNMF (weighted nonnegative matrix factorization) model to generate customers preference’s hierarchically structured hidden implicit features and product characteristics in each cluster, which reveals a deep relationship between them and regularizes the prediction procedure via an auxiliary parameter (tag information). The testimonies and empirical findings supported the viability of the proposed approach. Especially, MAE of the rating prediction was 0.8011 with 60% training dataset size, while the error rate was equal to 0.7965 with 80% training dataset size. Moreover, MAE rates were 0.8781 and 0.9046 in new 50 and 100 customer cold-start scenarios, respectively. The proposed model outperformed other baseline models that independently employed the major properties of customers, products, or tags in the prediction process.

Given a population S of N tags in an RFID system, the tag-information sampling problem is to randomly choose K distinct tags from S to form a subset T, and then inform each tag in T of a unique integer from 1,2,⋯ ,K. This is a fundamental problem in many real-time analysis applications of RFID systems, as it enables rapidly selecting a small random tag subset T from a large tag population S and collecting the tag-information from T for analyzing purposes. However, existing protocols for this problem are far from satisfactory due to high communication costs. This paper aims at solving this problem by using a small communication cost. First, a lower bound on communication cost, denoted by Clb, is obtained for the tag-information sampling problem. Then, a protocol Ps is proposed for solving the studied problem, and is proved to have a communication cost within a factor of 2 of the lower bound Clb. Lastly, extensive simulation results not only verify the theoretical properties of the proposed protocol but also demonstrate its advantages in comparison with the state-of-art protocols.

Conversational Recommender Systems (CRS) aim to provide high-quality items to users in fewer conversation rounds using natural language. Despite various attempts that have been made, there are still some problems: Previous CRS only learned item representations in a single knowledge graph and ignored item tags; information gaps exist in the same items from different knowledge graphs and information popularity both affect user preferences; system generated responses lack descriptiveness and diversity. To address these problems and fully utilize external knowledge, we propose a M ulti-source Information C ontrastive Learning C ollaborative A ugmented method ( MCCA ), which aims to mine the potential tag preferences of users in dialogues as well as enhance the accuracy of item representation and user preference modeling. Specifically, we utilize the obtained items and their tags to construct a new knowledge graph that incorporates movie tags. We design a M ulti-source I tem F usion mechanism ( MIF ) to bridge the information gaps between items from different knowledge graphs and then utilize unsupervised contrastive learning to enhance the items’ representation capability after MIF. Additionally, a M ulti- T ag F usion mechanism ( MTF ) is designed to combine user-perceived information (i.e., tag popularity) and keywords obtained from reviews to co-enhance user preference representations through items and tags, and to incorporate fused item and tag features into the conversation module. Extensive experiments on two datasets show that MCCA significantly outperforms state-of-the-art methods. The source code will be available at https://github.com/lhy-cqut/MCCA .

A social tagging system improves recommendation performance by introducing tags as auxiliary information. These tags are text descriptions of target items provided by individual users, which can be arbitrary words or phrases, so they can provide more abundant information about user interests and item characteristics. However, there are many problems to be solved in tag information, such as data sparsity, ambiguity, and redundancy. In addition, it is difficult to capture multi-aspect user interests and item characteristics from these tags, which is essential to the recommendation performance. In the view of these situations, we propose a tag-aware recommendation model based on attention learning, which can capture diverse tag-based potential features for users and items. The proposed model adopts the embedding method to produce dense tag-based feature vectors for each user and each item. To compress these vectors into a fixed-length feature vector, we construct an attention pooling layer that can automatically allocate different weights to different features according to their importance. We concatenate the feature vectors of users and items as the input of a multi-layer fully connected network to learn non-linear high-level interaction features. In addition, a generalized linear model is also conducted to extract low-level interaction features. By integrating these features of different types, the proposed model can provide more accurate recommendations. We establish extensive experiments on two real-world datasets to validate the effect of the proposed model. Comparable results show that our model perform better than several state-of-the-art tag-aware recommendation methods in terms of HR and NDCG metrics. Further ablation studies also demonstrate the effectiveness of attention learning.

Identifying the hidden features of items and users of a modern recommendation system, wherein features are represented as hierarchical structures, allows us to understand the association between the two entities. Moreover, when tag information that is added to items by users themselves is coupled with hierarchically structured features, the rating prediction efficiency and system personalization are improved. To this effect, we developed a novel model that acquires hidden-level hierarchical features of users and items and combines them with the tag information of items that regularizes the matrix factorization process of a basic weighted non-negative matrix factorization (WNMF) model to complete our prediction model. The idea behind the proposed approach was to deeply factorize a basic WNMF model to obtain hidden hierarchical features of user’s preferences and item characteristics that reveal a deep relationship between them by regularizing the process with tag information as an auxiliary parameter. Experiments were conducted on the MovieLens 100K dataset, and the empirical results confirmed the potential of the proposed approach and its superiority over models that use the primary features of users and items or tag information separately in the prediction process.

World Wide Web is a vast information resource that can be used in a broad range of applications. Web content is an efficient way to derive valuable information from webpages, and many efforts have been made on this subject. However, due to the increasing complexity of webpage technology, the existing methods cannot match quite well the requirements for the content extraction of webpages. This paper proposed an improved content extraction method for webpage based on Cx-Extractor, which is capable of dealing with content extraction for different types of webpages. Several improvements have been made for the proposed method: (1) The hyperlink tags are not removed directly to avoid mistaking the dense hyperlink groups for the main content. (2) The starting point of the main content is taken as the line number of tag-line-block whose size exceeds the threshold and thus the first few short texts of the main content can be retained. (3) The threshold value of tag-line-block for the main content is calculated automatically instead of being set manually. The above can improve the accuracy of the extracted content. Moreover, (4) the blank spaces in the original text of webpage are retained, which can increase the readability of the extracted content by avoiding connecting English words into pieces. (5) The multimedia information (e.g., pictures and videos) can be selectively retained by users, allowing for maximum flexibility and usage in multiple industries. The experimental results conducted on real-world webpages show that the proposed content extraction method works well for both single-content and multi-content webpages. Furthermore, the performance of the proposed content extraction method was compared with the Chinese extraction method called Cx-Extractor and the English extraction method called Readability. It is found that the proposed method in this study outperforms these two methods in precision, recall, and readability. In addition, the extraction efficiency of the proposed method is superior to that of the Readability method.

With the popularization of social media and the exponential growth of information generated by online users, the recommender system has been popular in helping users to find the desired resources from vast amounts of data. However, the cold-start problem is one of the major challenges for personalized recommendation. In this work, we utilized the tag information associated with different resources, and proposed a tag-based interactive framework to make the resource recommendation for different users. During the interaction, the most effective tag information will be selected for users to choose, and the approach considers the users’ feedback to dynamically adjusts the recommended candidates during the recommendation process. Furthermore, to effectively explore the user preference and resource characteristics, we analyzed the tag information of different resources to represent the user and resource features, considering the users’ personal operations and time factor, based on which we can identify the similar users and resource items. Probabilistic matrix factorization is employed in our work to overcome the rating sparsity, which is enhanced by embedding the similar user and resource information. The experiments on real-world datasets demonstrate that the proposed algorithm can get more accurate predictions and higher recommendation efficiency.

Radio frequency identification (RFID) systems suffer many security risks because they use an insecure wireless communication channel between tag and reader. In this paper, we analyze two recently proposed RFID authentication protocols. Both protocols are vulnerable to tag information leakage and untraceability attacks. For the attack on the first protocol, the adversary only needs to eavesdrop on the messages between reader and tag, and then perform an XOR operation. To attack the second protocol successfully, the adversary may execute a series of carefully designed challenges to determine the tag's identification.

Data extraction from the web pages is the process of analyzing and retrieving relevant data out of the data sources (usually unstructured or poorly structure) in a specific pattern for further processing, involves addition of metadata and data integration details for further process in the data workflow. This survey describes overview of the different web data extraction and data alignment techniques. Extraction techniques are DeLa, DEPTA, ViPER, and ViNT. Data alignment techniques are Pairwise QRR alignment, Holistic alignment, Nested structure processing. Query Result pages are generated by using Web database based on Users Query. The data from these query result pages should be automatically extracted which is very important for many applications, such as data integration, which are needed to cooperate with multiple web databases. New method is proposed for data extraction t that combines both tag and value similarity. It automatically extracts data from query result pages by first identifying and segmenting the query result records (QRRs) in the query result pages and then aligning the segmented QRRs into a table. In which the data values from the same attribute are put into the same column. Data region identification method identify the noncontiguous QRRs that have the same parents according to their tag similarities. Specifically, we propose new techniques to handle the case when the QRRs are not contiguous, which may be due to presence of auxiliary information, such as a comment, recommendation or advertisement, and for handling any nested structure that may exist in the QRRs.

Stochastic growth models were fitted to length-increment data of eastern king prawns, Melicertus plebejus (Hess, 1865), tagged across eastern Australia. The estimated growth parameters and growth transition matrix are for each sex representative of the species' geographical distribution. Our study explicitly displays the stochastic nature of prawn growth. Capturing length-increment growth heterogeneity for short-lived exploited species such as prawns that cannot be readily aged is essential for length-based modelling and improved management.