Personalized literature retrieval recommendation system framework design

					                                                                                                  Journal of
                                                                                                 Science and

2050-2311/Copyright © 2012 IE Enterprises ltd                                          Jour. of Comp. Sci. and Eng.
All right reserved                                                                        Vol. 1, Num.1, 0014–0017, 2012

          Personalized Literature Retrieval Recommendation System
                              Framework Design
                                                         Wenli Huaa, a*
    Anhui Vocational College of Electronic & Information Technology Department of Teaching and Scientific Research Bengbu Anhui 233030,


Personal recommendation for academic literature in vast digital literatures database is one of the hot issues in information
retrieval area. Common recommendation systems often missed literatures with “different characters” but semantic similarity.
The paper introduces the design scheme of academic literature recommendation system. User‟s 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 system;Literature;ODP Categories System;Cosine 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‟ and „information overload‟ phenomenon[1].
   A Literature personalized recommendation system is proposed to solve the above problem; it can find the
user‟s 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 and
digital literature sequence recommendation modules etc.

      * Corresponding author. Wenli Hua
      E-mail address:
      September 2012
                               Wenli Hua / Journal of Computational Science and Engineering 1:1 (2012) 0014–0017

  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 to
retrieve is shown in Figure 1. The basic strategy of literature retrieval is that the system retrieves matching
literature automatically according to the user‟s 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.

                    Collection of user information                                     Collection of literature information

                                                           Feature extraction

                                             Match of user preference and literature feature

                                                        Sequence recommendation

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 includes:collection 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.

                  Information collection                   Feature extraction                       Literature recommendation

         Extraction of        Extraction             Feature            Extraction               Match measure of           Sequence
             user            of literature         extraction of       of literature             literature and user     recommendation
         information         information               user               feature                     preference

Fig. 2. PRSSL Logical structure module picture of system
                               Wenli Hua / Journal of Computational Science and Engineering 1:1 (2012) 0014–0017

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) to
calculate 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 systems:indication 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 user‟s registration information and the keyword of the literature
which the user has published.
    Define vui= k1+k2+k3,k1, 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 1,and if the feature word wui appears in the registration information, then k1=0.281,
otherwise k1=0;if 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=0.704,otherwise k3=0。
   (2) Literature feature representation algorithm
   All digital literatures have Keywords part,it 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 the
literature features, where wpi is the literature keyword, define vpi=fni/(fn1+…+fnp), record the number of times
which 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 literature‟s features and the user preferences will be measured by using the cosine
similarity method,here make Vu=(vu1, vu2,...., vuu),VP= (vp1,vp2,…,vpp),in that Sim(U,P)=cos(Vu,Vp)=
  Vu  V p        . But there is a problem which needs to be addressed, the feature of V u and Vp are not necessarily the
| Vu |  | VP |
same, in fact, there are three relationships between both characteristics:same, 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 the
computing 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
follows:First 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
  Second step,standardized process U, P,to 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 V u
and VP respectively, prepare to calculate the cosine similarity. Forth step, calculate the cosine similarity.
                           Wenli Hua / Journal of Computational Science and Engineering 1:1 (2012) 0014–0017

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 standard,how 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 and VP into the weight about W.


  The work is supported by Humanity and Social Science foundation of Ministry of Education of China under
Grant No. 09YJC870001.


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


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

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