Personalized Literature Retrieval Recommendation System Framework Design

7/31/2019 Personalized Literature Retrieval Recommendation System Framework Design.

1/4

Journal ofComputational

Science andEngineering

www.jcseuk.com

2050-2311/Copyright 2012 IE Enterprises ltd Jour. of Comp. Sci. and Eng.All right reserved Vol. 1, Num.1, 00140017, 2012

Personalized Literature Retrieval Recommendation System

Framework Design

Wenli Huaa, a*

aAnhui Vocational College of Electronic & Information Technology Department of Teaching and Scientific Research Bengbu Anhui 233030,

China

Abstract

Personal recommendation for academic literature in vast digital literatures database is one of the hot issues in informationretrieval area. Common recommendation systems often missed literatures with different characters but semantic similarity.

The paper introduces the design scheme of academic literature recommendation system. Users interest model and feature of

academic literature were studied from semantic view; furthermore similarity measure based on semantic was discussed.

Academic Literature Recommendation System was proposed based on analyzing the recommendation process.

Keywords: Recommendation systemLiteratureODP Categories SystemCosine similarity

Due to the huge amount of digital literature resources available, researchers seeking specific pieces of

literature are often unable to quickly locate the literature to which they are interested, resulting in the

information loss andinformation overload phenomenon[1].

A Literature personalized recommendation system is proposed to solve the above problem; it can find the

users preference from the collected user information and then recommend digital document information to the

user from the existing mass literature, thus greatly reducing the time the user spends waiting for retrieval.

We combined elements from previous studies into this field and designed a framework for the literature query

recommendation system. This framework mainly contains: user information collecting, user preferences feature,

resource feature extraction of digital literature, similarity measurement of the literature and user preferences anddigital literature sequence recommendation modules etc.

* Corresponding author. Wenli Hua

E-mail address: [email protected]

September 2012


2/4

Wenli Hua / Journal of Computational Science and Engineering 1:1 (2012) 00140017

The design of this system framework has provided the proof for the detailed design and development of a

prototype system; it also has the certain reference value for the promotion and application of the literature

recommendation theory.

1. The overall system function

The design of the system framework uses the process of literature retrieval as the proof of the module division,

the process of using the PRSSL (the Personal Recommendation System for Scholarly Literature) system toretrieve is shown in Figure 1. The basic strategy of literature retrieval is that the system retrieves matching

literature automatically according to the users preference information and then recommends the results to the

user according to the sequence of matching level. In order to achieve the above functions, the main problems

that the system needs to solve are: extraction of user interest preference, extraction of literature feature,

similarity measure and recommendation of literature sequence etc.

Fig. 1. The process of literature query recommendation

2. The logical structure of the PRSSL

After the demand analysis of PRSSL [2] its main logic module includescollection of user information,

extraction of user preference feature, collection of literature information, extraction of literature feature,

literature recommendation modules etc. The module picture of logical structure is shown in figure 2.

Fig. 2. PRSSL Logical structure module picture of system

PRSSL

Information collection

Extraction of

user

information

Feature extraction

Feature

extraction of

user

preference

Extraction

of literature

information

Literature recommendation

Extraction

of literature

feature

Match measure of

literature and user

preference

Sequence

recommendation

Collection of user information Collection of literature information

Feature extraction

Match of user preference and literature feature

Sequence recommendation


3/4


3 PRSSL Function design

3.1 Function design of main module of system

According to the logical structure of the PRSSL system, its core functions include three parts: information

collection, feature extraction, literature recommendation. And the difficulties of the algorithm design contain

three aspects: indication and extraction of user preferences, indication and extraction of literature feature,matching measure of literature and user preference. PRSSL adopts the form of vector to represent user

preference U and literature feature P respectively, uses the method of cosine similarity measure cos (Vu, Vp) tocalculate the matching level of user preferences and literature features, and eventually recommends literature

according to the sequence of match level.

3.2 Core algorithm design of system

Three of the core algorithm need to be solved in PRSSL systemsindication of user preferences, indication

of literature features and the match level of user preferences and literature features.

(1) The algorithm of user preferences indication

PRSSL uses feature vector U=((wu1,vu1),(wu2,vu2),,(wuu,vuu)) to indicate the user interest preferences,

where wui is the feature word, vui is the level of interest which the user has placed on the feature word, wu i, wui

is from the keyword of research area in the users registration information and the keyword of the literaturewhich the user has published.

Define vui= k1+k2+k3k1, k2, k3 as representations of registration information, published literature, influence

level of current retrieval method on interestingness, the values of vui, k1, k2, k3 are set mainly according to the

experiment results and the purpose to maintain the consistence of order of magnitude with literature feature

vector. In 0 vui 1and if the feature word wui appears in the registration information, then k1=0.281,

otherwise k1=0if the feature word wui appears in the published literature, then k2=0.015*(includes the number

of published literature of wi/ the total number of published literature)otherwise k2=0 wui if the feature word

wui is included in the retrieval formula then k3=0.704otherwise k3=0

(2) Literature feature representation algorithm

All digital literatures have Keywords partit is feasible to chose these keywords as the carrier of literature

feature description. PRSSL uses the number of times which the keyword appears in the literature as the proof to

measure the literature feature, use vector P=((wp1, vp1),(wp2, vp2),,(wpp, vpp)) as the tool to indicate theliterature features, where wpi is the literature keyword, define vpi=fni/(fn1++fnp), record the number of timeswhich the keywords wpi appeared in the literature as fni. It is not difficult to see from the above definitions and

agreement that vpi represents the weight that the keyword number i possessed in literature features.

(3) Match measure of literature and user preference

The match level of the literatures features and the user preferences will be measured by using the cosine

similarity methodhere make Vu=(vu1, vu2,...., vuu)VP= (vp1,vp2,,vpp)in that Sim(U,P)=cos(Vu,Vp)=

| | | |

u p

u P

V V

V V

. But there is a problem which needs to be addressed, the feature of V u and Vp are not necessarily the

same, in fact, there are three relationships between both characteristicssame, similar or different. The so-

called similar means in terms of disciplines, the relationship between the two features may be the conceptual

relationship between father and son, for example, the relationship between association rule and data mining can

be seen as a father-son relationship. Based on this consideration, prior to the calculating the match level of thecomputing literature features and user preferences, first, we need to carry out the unified standardized process of

characteristics to U,P, that is U,P should have the same characteristics. The process strategy of this part is as

followsFirst step, generalizing each feature of user preference feature vector by using the ODP directory

systems ideology [3]all the generalization paths of characteristics constitute a generalized tree structure, the

root of the tree is the name of the second stage subject in research area subject which the user specified in the

registration.

Second stepstandardized process U, Pto guarantee that U, P has the same feature item, therefore this

requires building the W union of U, P, the building process of W can be based on U, based on the basis of

conceptual generalization, gradually add W in the feature item of P. Third step, use W as a standard, regulate Vuand VP respectively, prepare to calculate the cosine similarity. Forth step, calculate the cosine similarity.


4/4


4 Research outlook

The main issues that need to be addressed next are: (1) How to determine the structure of the feature vector

conceptual generalization tree ; (2) How to derive the union W by the conceptual generalized tree; (3) Using W

as a standardhow to standardize Vu and VP. Here the key issue to be solved is how to transform the weight of

all feature items of original Vu andVP into the weight about W.

Acknowledgement

The work is supported by Humanity and Social Science foundation of Ministry of Education of China under

Grant No. 09YJC870001.

References

[1] Genyuan Lai. Research of cross-language recommended model of scientific literature. Journal of Library Science in China 2012

38(198)71-78.

[2] Yong Li, DezhiXu, Yong Zhang etc. Aarticle recommendation algorithm in VRE based on content filtering .Application Research of

Computers, 2007, 24(9): 58-61..

[3] http://www.dmoz.org/[OL]

Biography

Wenli Hua(1969.2-), male, postgraduate, assistant professor, current research interests are: recommendation

system, data mining, Email: [email protected].

Documents

Personalized Literature Retrieval Recommendation System Framework Design