Items in real-world recommender systems exhibit certain hierarchical structures. Similarly, user preferences also present hierarchical structures. Recent studies show that incorporating the explicit hierarchical structures of items or user preferences can improve the performance of recommender systems. However, explicit hierarchical structures are usually unavailable, especially those of user preferences. Thus, there's a gap between the importance of hierarchical structures and their availability. In this paper, we investigate the problem of exploring the implicit hierarchical structures for recommender systems when they are not explicitly available. We propose a novel recommendation framework HSR to bridge the gap, which enables us to capture the implicit hierarchical structures of users and items simultaneously. Experimental results on two real world datasets demonstrate the effectiveness of the proposed framework.

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Exploring implicit hierarchical structures for recommender systems

Items in real-world recommender systems exhibit certain hierarchical structures. Similarly, user preferences also present hierarchical structures. Recent studies show that incorporating the hierarchy of items or user preferences can improve the performance of recommender systems. However, hierarchical structures are often not explicitly available, especially those of user preferences. Thus, there's a gap between the importance of hierarchies and their availability. In this paper, we investigate the problem of exploring the implicit hierarchical structures for recommender systems when they are not explicitly available. We propose a novel recommendation framework to bridge the gap, which enables us to explore the implicit hierarchies of users and items simultaneously. We then extend the framework to integrate explicit hierarchies when they are available, which gives a unified framework for both explicit and implicit hierarchical structures. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework by incorporating implicit and explicit structures.

Exploring Hierarchical Structures for Recommender Systems

This paper proposes a new approach to collaborative profile recommendation using a hierarchical structure for user modeling. In an information retrieval system a hierarchical user profile, used to personalize the document retrieval process, is being recommended to a new user based on profiles of other, similar users. Using methodology from the Knowledge Integration domain, four criteria are defined and analyzed to complete the aim of recommendation: Reliability is required for maintaining the correct structure of the profile, O1 and O2 Optimality postulates are required to calculate the best output profile by minimizing distances to other profiles, and Conflict Solution is used to better represent situations inherent to profile recommendation. Based on those criteria, four algorithms are proposed: O1 and O2 algorithms and modified O1 and O2 algorithms. These algorithms are further analyzed to check if they provide good recommendation.

A method for collaborative recommendation using knowledge integration tools and hierarchical structure of user profiles

With fast-growing technology, online social networks (OSNs) have exploded in popularity over the past few years. The pivotal reason behind this phenomenon happens to be the ability of OSNs to provide a platform for users to connect with their family, friends, and colleagues. The information shared in social network and media spreads very fast, almost instantaneously which makes it attractive for attackers to gain information. Secrecy and surety of OSNs need to be inquired from various positions. There are numerous security and privacy issues related to the user’s shared information especially when a user uploads personal content such as photos, videos, and audios. The attacker can maliciously use shared information for illegitimate purposes. The risks are even higher if children are targeted. To address these issues, this paper presents a thorough review of different security and privacy threats and existing solutions that can provide security to social network users. We have also discussed OSN attacks on various OSN web applications by citing some statistics reports. In addition to this, we have discussed numerous defensive approaches to OSN security. Finally, this survey discusses open issues, challenges, and relevant security guidelines to achieve trustworthiness in online social networks.

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Online social networks security and privacy: comprehensive review and analysis

An intelligent movie recommendation system through group-level sentiment analysis in microblogs

Complete definition and description of integration function and postulates.Proposition of Aug function, which determines new knowledge gained in integration.Consistent description of multi-level integration and necessary postulates.Example of multi-level integration for two specific cases in tree integration.Multiple examples of integration function and criteria for trees. Techniques for processing knowledge in collectives are more and more needed because of rapidly increasing number of autonomous sources of knowledge in the world. Collective intelligence, among others, deals with creating the knowledge of a collective which is consistent and complete. This means that it should contain all elements not belonging to the knowledge of collective members, but can be inferred on the basis of knowledge of them. For this process the methodologies for knowledge integration seem to be very useful. In this paper the authors present a framework for integrating knowledge of a collective which shows that knowledge of a collective should not be a normal sum of knowledge of its members. The model for knowledge integration using complex hierarchical structures has been also presented and analyzed.

Integration computing and collective intelligence

We present a group dynamics model that shows knowledge integration as a process occurring over time. As each individual in the group contact others, his own knowledge changes, and over time the col...

Application of collective knowledge diffusion in a social network environment

The process of integrating data in a hierarchical structure is usually based on algorithms designed for one specific task; for example, for eXtendable Markup Language (XML) schema integration. In this article, a more general approach for integration of data in a hierarchical structure is proposed using a generalized tree structure called a complex tree. For this aim we define a set of criteria for integration. These criteria are completeness, precision, and optimality. A simple algorithm based on these criteria is presented and the advantages of the approach are discussed.

Marcin Maleszka

Papers

A method for collaborative recommendation using knowledge integration tools and hierarchical structure of user profiles

Integration computing and collective intelligence

Application of collective knowledge diffusion in a social network environment

A method for complex hierarchical data integration

Observing collective knowledge state during integration