IP 3 Continuation of IP1 and IP 2

Hybrid Scalable Researcher Recommendation System Using Page Rank Algorithm 1

Hybrid Scalable Researcher Recommendation System Using Page Rank Algorithm 5

Hybrid Scalable Researcher Recommendation System Using Page Rank Algorithm

Dinesh Kalla

Colorado Technical University

CS 857: Business Intelligence

Dr D Revenaugh

3rd March 2021

Problem Statement

Page rank algorithm is used by Google search engine to rank websites based on their importance. Google scholar is the commonly used recommender system for research papers. The increasing number of research papers makes it difficult for novice academic researchers to obtain relevant scholarly articles with ease. The current recommender system does not have scalable functionality to cater to the increasing number of journals and articles published daily in the global arena. Other recommender systems such as Microsoft Academic, Web of Science, and PubMed databases use the collaborative approach, hindered by data protection legislation. Additionally, the said academic search engines domicile in one particular field of study. My research paper will focus on resolving this problem by recommending a hybrid scalable search engine that will utilize a page rank algorithm to determine the relevance of academic documents and display them in the order of importance. It will combine content-based and co-citation recommendations to produce related documents, including the recently published ones, without compromising the quality of recommendations.

Justification / Literature Review

Recommender systems have been applied in a variety of applications such as Twitter and Netflix. For instance, Twitter uses PageRank to suggest new followers to its users. To generate this suggestion, the application is designed in such a way it performs personalized PageRank by establishing two copies of each active user; a content producer and a content consumer. In this sense, a user can post a tweet or, in other cases, read a tweet by other users. A content produced by the user can be retweeted, further-reaching a new audience. This developing network can leverage on PageRank algorithm to suggest new followers.

Another application scenario is in movie rental sites such as Netflix, where a user considers a movie to be relevant if other users like the movie. The second approach to like a movie is when a mutual friend likes movies that we both have interests. With these two approaches, Netflix can create a personalized ranking for users on the film they want using the PageRank algorithm. The ‘behavior’ of other mutual users on the platform enables the system to determine what other users might like.

Data mining systems also utilize a similar approach where user behaviors can predict what products they might like (Bathrinath, 2019). In Business intelligence systems can track user preferences and suggest new products to the user in priority. Data mining is categorized into three groups, namely, web content mining, Web usage mining, and web structure mining. Web structure mining uses the PageRank concept to analyze and rank web pages in an internet search.

Also, a page ranking algorithm is applied to academic services such as e-libraries, e-learning, online purchasing systems, and news synthesis. In this way, users are provided a personalized recommendation based on their liking behavior of a certain service or product. In this paper, I will focus on offering an alternative solution to the problematic collaborative filtering (CF) by taking into account the freshness of contents, scalability feature, improved user satisfaction, and turnaround time (computational time ) to generate a search list.

Methodology

I propose to use a hybrid recommender system that utilizes both content-based (CB)s and co-citation-based (CcB)to generate a search of items. CcB is computationally time-consuming and generates low-quality recommendations, whereas CB uses metadata, keywords, articles, and abstracts to create fresh content and huge coverage. CcB uses reference information in a paper to make a recommendation. This empirically resembles human behavior when it's searching for related documents. The referencing feature of CcB is a major drawback considering most old papers were published in hard copies, and their reference is not available digitally

On the other hand, CB resolves some of the inefficacies of CcB, i.e., complexity issues, privacy concerns, and cold start problems. CB uses information available on the web pages, such as metadata, keywords, title, and abstract. Cluster paper embedding and generating paper embedding shall be used to determine the relevance of a paper in the search domains in larger data sets (Xing et al., 2018). This augments well with my research that seeks to provide a scalable recommender system that can utilize content-based and co-citation-based to improve on quality and hierarchical clustering based on relevance. Combining both CB and CcB generates unified search items by ordering related papers from both lists based on congruency to the document in question.

References

Bathrinath (2019). PageRank Algorithm-Based Recommender System Using Uniformly Average Rating Matrix. https://www.igi-global.com/chapter/pagerank-algorithm-based-recommender-system-using-uniformly-average-rating-matrix/216694

Xing, W., & Ghorbani, A. (2018, May). It weighted the PageRank algorithm. In Proceedings. Second Annual Conference on Communication Networks and Services Research, 2004. (pp. 305-314). IEEE.