Personal Web Revisitation by Context and Content Keywords with Relevance Feedback

Personal Web Revisitation by Context and Content Keywords with Relevance Feedback

ABSTRACT:

Getting back to previously viewed web pages is a common yet uneasy task for users due to the large volume of personally accessed information on the web. This paper leverages human’s natural recall process of using episodic and semantic memory cues to facilitate recall, and presents a personal web revisitation technique called WebPagePrev through context and content keywords. Underlying techniques for context and content memories’ acquisition, storage, decay, and utilization for page re-finding are discussed. A relevance feedback mechanism is also involved to tailor to individual’s memory strength and revisitation habits. Our 6-month user study shows that: (1) Compared with the existing web revisitation tool Memento, History List Searching method, and Search Engine method, the proposed WebPagePrev delivers the best re-finding quality in finding rate (92.10%), average F1-measure (0.4318) and average rank error (0.3145). (2) Our dynamic management of context and content memories including decay and reinforcement strategy can mimic users’ retrieval and recall mechanism. With relevance feedback, the finding rate of WebPagePrev increases by 9.82%, average F1-measure increases by 47.09%, and average rank error decreases by 19.44% compared to stable memory management strategy. Among time, location, and activity context factors in WebPagePrev, activity is the best recall cue, and context+content based re-finding delivers the best performance, compared to context based re-finding and content based re-finding.

PROJECT OUTPUT VIDEO: (Click the below link to see the project output video):

EXISTING SYSTEM:

  • In the literature, a number of techniques and tools like bookmarks, history tools, search engines, metadata annotation and exploitation, and contextual recall systems have been developed to support personal web revisitation.
  • The most closely related work of this study is Memento system, which unifies context and content to aid web revisitation. It defined the context of a web page as other pages in the browsing session that immediately precede or follow the current page, and then extracted topic-phrases from these browsed pages based on the Wikipedia topic list.
  • Other closely related work enabled users to search for contextually related activities (e.g., time, location, concurrent activities, meetings, music playing, interrupting phone call, or even other files or web sites that were open at the same time), and find a target piece of information (often not semantically related) when that context was on. This body of research emphasizes episodic context cues in page recall.

DISADVANTAGES OF EXISTING SYSTEM:

  • Uneasy task for users
  • Large Volume of data, makes more complex
  • Poor finding rate
  • Low F1-measure

PROPOSED SYSTEM:

  • Preparation for web revisitation. When a user accesses a web page, which is of potential to be revisited later by the user (i.e., page access time is over a threshold), the context acquisition and management module captures the current access context (i.e., time, location, activities inferred from the currently running computer programs) into a probabilistic context tree. Meanwhile, the content extraction and management module performs the unigrambased extraction from the displayed page segments and obtains a list of probabilistic content terms.
  • The probabilities of acquired context instances and extracted content terms reflect how likely the user will refer to them as memory cues to get back to the previously focused page.
  • Web revisitation. Later, when a user requests to get back to a previously focused page through context and/or content keywords, the re-access by context keywords module and re-access by content keywords module search the probabilistic context tree repository and probabilistic term list repository, respectively.

ADVANTAGES OF PROPOSED SYSTEM:

  • This paper explores how to leverage our natural recall process of using episodic and semantic memory cues to facilitate personal web revisitation. Considering the differences of users in memorizing previous access context and page content cues, a relevance feedback mechanism is involved to enhance personal web revisitation performance.
  • We present a personal web revisitation technique, called WebPagePrev, that allows users to get back to their previously focused pages through access context and page content keywords. Underlying techniques for context and content memories’ acquisition, storage, and utilization for web page recall are discussed.
  • Dynamic tuning strategies to tailor to individual’s memorization strength and recall habits based on relevance feedback (e.g., weight preference calculation, decay rate adjustment, etc.) are developed for performance improvement.
  • We evaluate the effectiveness of the proposed technique WebPagePrev, and report the findings (e.g., the importance of context and content factors) in web revisitation.

SYSTEM ARCHITECTURE:

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : JAVA/J2EE
  • Tool : Netbeans 7.2.1
  • Database : MYSQL

REFERENCE:

Li Jin, Gangli Liu, Chaokun Wang and Ling Feng, Senior Member, IEEE, “Personal Web Revisitation by Context and Content Keywords with Relevance Feedback”, IEEE Transactions on Knowledge and Data Engineering, 2017.

 

Modeling Information Diffusion over Social Networks for Temporal Dynamic Prediction

Modeling Information Diffusion over Social Networks for Temporal Dynamic Prediction

Modeling Information Diffusion over Social Networks for Temporal Dynamic Prediction

ABSTRACT:

How to model the process of information diffusion in social networks is a critical research task. Although numerous attempts have been made for this study, few of them can simulate and predict the temporal dynamics of the diffusion process. To address this problem, we propose a novel information diffusion model (GT model), which considers the users in network as intelligent agents. The agent jointly considers all his interacting neighbors and calculates the payoffs for his different choices to make strategic decision. We introduce the time factor into the user payoff, enabling the GT model to not only predict the behavior of a user but also to predict when he will perform the behavior. Both the global influence and social influence are explored in the time dependent payoff calculation, where a new social influence representation method is designed to fully capture the temporal dynamic properties of social influence between users. Experimental results on Sina Weibo and Flickr validate the effectiveness of our methods.

EXISTING SYSTEM:

  • In “Scalable influence maximization for prevalent viral marketing in large-scale social networks”: Influence maximization, defined by Kempe, Kleinberg, and Tardos (2003), is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. The scalability of influence maximization is a key factor for enabling prevalent viral marketing in large-scale online social networks.
  • Prior solutions, such as the greedy algorithm of Kempe et al. (2003) and its improvements are slow and not scalable, while other heuristic algorithms do not provide consistently good performance on influence spreads.
  • In existing system, the authors design a new heuristic algorithm that is easily scalable to millions of nodes and edges in our experiments. The system has a simple tunable parameter for users to control the balance between the running time and the influence spread of the algorithm.

DISADVANTAGES OF EXISTING SYSTEM:

  • How to model the process of information diffusion in social networks is a critical research task.
  • Although numerous attempts have been made for this study, few of them can simulate and predict the temporal dynamics of the diffusion process.

PROPOSED SYSTEM:

  • In this paper, we propose a novel information diffusion model (GT model) for temporal dynamic prediction. In contrast to traditional theory-centric models, the GT model regards the users in the network as intelligent agents. It can capture both the behavior of individual agent and the strategic interactions among these agents. By introducing the time-dependent payoffs, the GT model is able to predict the temporal dynamics of the information diffusion process. Different from most data-centric models, the GT model can not only predict whether a user will perform a behavior but also can predict when he will perform it.
  • In the proposed GT model, the diffusion process unfolds in discrete time-steps t, and begins from a given initial active user set. When a user v observes a piece of information at time t, he calculates his payoffs for different choices depending on his neighbors’ status so as to make strategic decision.

ADVANTAGES OF PROPOSED SYSTEM:

  • We propose a novel information diffusion model (GT model), where, between different choices (behaviors), the user jointly considers all his interacting neighbors’ choices to make strategic decisions that maximizes his payoff.
  • We propose a time-dependent user payoff calculation method in the GT model by exploring both the global influence and social influence.
  • We propose a new social influence representation method, which can accurately capture the temporal dynamic properties of social influence between users.
  • We conduct experiments on datasets. The comparison results with closely related work indicate the superiority of the proposed GT model.

SYSTEM ARCHITECTURE:

Modeling Information Diffusion over Social Networks

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : JAVA/J2EE
  • Tool :         Netbeans 7.2.1
  • Database : MYSQL

REFERENCE:

Dong Li, Zhiming Xu, Yishu Luo, Sheng Li, Anika Gupta_Katia Sycara, Shengmei Luo, Lei Hu, Hong Chen, “Modeling Information Diffusion over Social Networks for Temporal Dynamic Prediction”, IEEE Transactions on Knowledge and Data Engineering, 2017.