Web Page Recommendation System

October 7, 2017 Author: munishmishra04_3od47tgp
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Recommender systems have become an important study area recently. The increasing amount of web content on web sites makes the recommender system an essential part of web sites. Recommender systems try to direct users to where they would like to go without getting the user lost in the huge amounts of information on the web site. The recommending of books, CDs and other products at Amazon.com is an example of such a system. Web-page recommendation has proved in recent years to be a valuable means of helping Web users by providing useful and effective recommendations or suggestions. The core techniques in web-page recommendation are the learning and prediction models which learn users’ behaviour and evaluate what users would like to view in the future. In particular, it can suggest interesting items from a large set of items based on the knowledge gained about an active user. Web-page recommendation can automatically recommend Web-pages that are most interesting to a particular user based on the user’s current Web navigation behaviour. Good Web-page recommendations can improve website usage and Web user satisfaction.

Through the continuous development of internet knowledge, Web has become a huge repository of information and keeps growing exponentially under no editorial control. However the human capability to read, access and understand content remains constant. Hence it became more challenging to the Website owners to selectively provide relevant information to the people with diverse needs. Modelling and analyzing Web navigational behavior is helpful in understanding the type of information online user’s demand. This motivated researchers to provide Web personalized online services such as Web recommendations to alleviate the information overload problem and provide tailored Web experience to the Web users. Web-page recommendations based on the Web Access Sequences (WAS) from web usage data. WAS is nothing but the sequences of visited web-pages by user’s during a session. By using this approach, given the current visited web-page and k-previously visited pages, the web-pages that will be visited in the next navigation step can be predicted.

Web Recommendation system is a specific type of information filtering system technique that attempts to predict the user next browsing activity then recommend to the user web pages items that are likely to be of interest to the user. A recommender system is a typical software solution used in e-commerce for personalized services. Based on the customer preferences, it helps to find the products they would like to purchase by providing recommendations and is particularly useful in ecommerce sites that offer millions of products for sale.

Definition: Web Page Recommendation System

Along with the development of recommender systems, Web-page recommendation has become increasingly popular, and is shown as links to related stories, related books, or most viewed pages at websites. When a user browses a website, a sequence of visited Web-pages during the session (the period from starting, to exiting the browser by the user) can be generated. This sequence is organized into a Web session ​\( S =(d_1,d_2,d_3,…..d_k) \)​, where ​\( d_i \) (i=[1,…k]) is the page ID of the ​\( i^{th} \)​ visited Web-page by the user. The objective of a Webpage recommender system is to effectively predict the Web-page or pages that will be visited from a given Web-page of a website. There are a number of issues in developing an effective Web-page recommender system, such as how to effectively learn from available historical data and discover useful knowledge of the domain and Web-page navigation patterns, how to model and use the discovered knowledge, and how to make effective Web-page recommendations based on the discovered knowledge. Recommendation system is an essential application of web mining. That is frequently used with a number of applications which not only helps to recommend a web page in a recommendation engine but also contributes to enhance the performance of the prediction system and web servers. These techniques are also used for enhancing the predictive modeling, user navigational behavior analysis and web pre-fetching and caching.

Categories of Web Page Recommendation System

A recommendation system can be developed in a number technique. According to behaviour of recommendation system can be classify into following categories.

  • Content based Recommendation: Similar items to the ones the user preferred in the past are generated as a recommendation.
  • Collaborative Recommendation: Items preferred by the people who have the similar taste to the user are generated as a recommendation.
  • Hybrid Approach: The above recommendation methods are combined in this approach.

Utilization or Applications of Recommendation Systems

There is different utilization of proposal frameworks are accessible; a few essential uses of suggestion frameworks are given as:

  1. Product Recommendations: Most of the time the recommendation systems are used with e-commerce based online stores to recommend a product to end user. For example how an online store suggest a returning user with dissimilar products. That is possible with observation of user search and their product buying capability.
  2. Movie Recommendations: Now in these days a number of online movie stores and online movie watching applications are also available. These applications are offering their consumers to watch the movies according to their interest. Additionally new appeared movies are also recommended to watch according to the users past interests. Sometimes these applications recommend movies much precisely.
  3. News Articles: In News websites or applications a number of different kinds of articles are published. Additionally browsing of all the articles to an online reader for finding the News according to their interest is a time consuming and frustrating task. Therefore the News publishers also implement the recommendation systems with their web applications that help to identify articles of interest to readers. These recommendation systems are developed on the basis of articles that a user read in past and the domain which is always explored by the given user.


[1] Gediminas Adomavicius and Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions”. IEEE Transactions on Knowledge and Data Engineering, pages 734–749, 2005 Volume 17.
[2] G. Linden, B. Smith, and J. York, Amazon.com Recommendations: Item-to-Item Collaborative Filtering, IEEE Internet Computing, Jan. /Feb. 2003
[3] Dubey, Purvi, and Pramod S. Nair. “Recommendation System for Web Mining: A Review.” International Journal of Computer Applications 109.11 (2015): 1-6.
[4] Gündüz-Ögüdücü, Şule. “Web page recommendation models: theory and algorithms.” Synthesis Lectures on Data Management 2.1 (2010): 1-85.
[5] Sawadsky, Nicholas, Gail C. Murphy, and Rahul Jiresal, “Reverb: Recommending code-related web pages”, Proceedings of the 2013 International Conference on Software Engineering. IEEE Press, 2013.

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