Nowadays advent of new technologies users can use the internet and play any type of music from anywhere, anytime searching song it very easy. Searching songs, videos and movies by using keyword-based searching method it’s very most natural and simple way to searching it. In proposed system the recommendation is based on users profile and users search history is very most challenging task in this system. Hybrid recommendation mechanism to search song by giving text input by user as well as it recommends user by doing effective ranking of the results obtained. User can easily search songs, videos, movies through text recognition system. In order to perform a search, the users can enter the song title or artist. The keyword-search based method is providing better efficiency and accuracy for result. The main function of proposed system is to perform song searching based on text input giving by the user is easy and effective searching of songs. In proposed system we use the information from a user’s search history as well as the common properties of users with similar backgrounds. The keyword-based retrieval system is very simple and easy and is very helpful to all people. The proposed system uses the combination of songs, videos as well as movies are more intensive and efficient method of search using text query. The pattern of sorting text and audio information is based on music library and re-ranking of music search history. The proposed system is providing the security of our system is very innovative and challenging task in this system. Proposed system providing suggestions and does not need to enter the proper keyword to search songs, videos, movies.

1. International Journal of Engineering Science and Computing, May 2016 4595 http://ijesc.org/
DOI 10.4010/2016.1146
ISSN 2321 3361 © 2016 IJESC
Effective Results of Searching Song for Keyword-Based Retrieval
Systems Using a Hybrid Recommendation Mechanism
Suraj S. Ligade1
, Anita T. Shinde2
, Ashvini A. Yendhe3
, Deepa B. Mane4
UG Scholar1, 2, 3
, Assistant Professor4
Department of Information Technology
APCOER, Pune, Savitribai Phule Pune University Pune, Maharashtra, India
suraj.ligade01@gmail.com1
, shindeanita02@gmail.com2
, ashu11101992@gmail.com3
, deepabmane@gmail.com4
Abstract:
Nowadays advent of new technologies users can use the internet and play any type of music from anywhere, anytime
searching song it very easy. Searching songs, videos and movies by using keyword-based searching method it’s very most natural
and simple way to searching it. In proposed system the recommendation is based on users profile and users search history is very
most challenging task in this system. Hybrid recommendation mechanism to search song by giving text input by user as well as it
recommends user by doing effective ranking of the results obtained. User can easily search songs, videos, movies through text
recognition system. In order to perform a search, the users can enter the song title or artist. The keyword-search based method is
providing better efficiency and accuracy for result. The main function of proposed system is to perform song searching based on
text input giving by the user is easy and effective searching of songs. In proposed system we use the information from a user’s
search history as well as the common properties of users with similar backgrounds. The keyword-based retrieval system is very
simple and easy and is very helpful to all people. The proposed system uses the combination of songs, videos as well as movies
are more intensive and efficient method of search using text query. The pattern of sorting text and audio information is based on
music library and re-ranking of music search history. The proposed system is providing the security of our system is very
innovative and challenging task in this system. Proposed system providing suggestions and does not need to enter the proper
keyword to search songs, videos, movies.
Keywords: Hybrid Recommendation, keyword search based method, music library, re-ranking of music search history, text
recognition.
I. INTRODUCTION
The keyword-based retrieval method is used for
searching songs, videos and movies. This method is helpful to
all people and easy to use. In existing system necessary to type
proper keyword to obtain result and there is no
recommendation used in this system. In existing system there
is no use of predictions to search songs and existing profile
based custom-made web search do not support runtime
profiling. The users search history is not store in system so the
system is provided the generic result to the user. The existing
methods do not take the customization of privacy
requirements. In proposed system different types of categories
are used such as songs, videos, movies, etc. In proposed
system the hybrid recommendation is used and this task is
most challenging task in this system. The naïve bays algorithm
is used in keyword-based retrieval method for classification of
songs, videos, movies. The keyword method is used to search
songs is most natural way for simple and easy searching task.
The personalized web search is used in proposed system
because improving the quality of various search services. A
users need to register personal information and interest in the
profile. When user enters the song related keyword system
returns result to the user or provide external links related to
song. This system gives the user to entering choices like film,
singer, actor or any other song, video, movie related
information. The proposed system provides privacy protection
in personalized web search applications that model user
preferences as user profiles.
The proposed system gives the personalising search
based on user search history. The personalized search search
based method is depends on users profile and description of
users interests. In proposed system the gathering of users
information for personalized search. The user profile is based
on their activity and user searching site itself and this profile is
providing the personalized search result. The proposed system
help users to find useful information on the World Wide Web,
when the same query is submitted by different users. The
typical search engines returns the same results of who
submitted the query. The proposed system find the search
results according to the users relevant information without use
any effort, and then verify the effectiveness of our proposed
approaches. In previous system song searching by title of song
is important topic. The algorithms used in present system are
not containing efficiency and better accuracy. In proposed
system using the Naïve Bayes algorithm is provide better
efficiency and accuracy to search the songs by music
recommendations. This system is using the query by keyword
based method and matching model. This system is based on
the Naïve Bayes algorithm and improving the ranking result
by local sensitive hashing algorithm. The validity of the
algorithm is obtainable by the prototype of query by keyword
system. There are three levels of musical features physical
feature, acoustic feature and perceptual feature. The physical
features express the audio content on the format of flow
media.
II. REVIEW LITERATURE
Ning -Han Liu has projected the method of querying by
singing or humming is the natural and simple method to
perform music search. Operator does not need to know the
title of song or name of the artist to perform search. It has
Research Article Volume 6 Issue No. 5
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become more important for users to be able to operate their
devices without manual input commands by hand. Author
combines the technique with query by singing/humming [1].
Through their experiments, we confirmed that the
methods proposed can considerably improve the search
accuracy. They used search history for ranking calculation.
The search system is delayed by the cold start effect, when a
operator performs little or no searches in the past, it would
cause the accuracy rate to remain at its current level and not
improve.
Micro Speretta and Susan Gauch explain the personalizing
search based on user search past history. The personalized
search based method is depends on users profiles and
description of user interests. Many approaches to creating user
profiles capture user information through proxy servers (to
capture surfing histories) or desktop bots (to capture all
actions on a personal workstation). These both require
participation of the operator to install the proxy server or the
bot. In this study, we explore the use of a less-invasive means
of collecting user data for personalized search. In particular,
we build user profiles based on activity at the search site itself
and training the use of these profiles to provide personalized
search results. These user profiles were created by classifying
the information into ideas from the Open Directory Project
thought hierarchy and then used to re-rank the search results.
User feedback was collected to compare Google’s original
rank with our new rank for the results examined by users. We
initiate that queries were as effective as snippets when used to
generate user profiles and that our personalized re-ranking
resulted in a 39% development in the rank-order of the user-
selected results.
Kazunari Sugiyama proposed Web search engines help users
find valuable info on the World Wide Web (WWW).
However, when the same query is submitted by different
users, typical search engines return the similar result
regardless about who submitted the query. Generally, each
user has different information requirements for his/her query.
Therefore, the search results should be adapted to users with
different information needs. In this paper, we first suggest
several approaches to adapting search results according to
each user’s need for relevant information without any user
effort, and then verify the efficiency of our proposed methods.
Experimental results show that search systems that adapt to
each user’s preferences can be achieved by building user
profiles based on modified collaborative filtering with detailed
analysis of user’s browsing history in one day.
Xuehua Shen Implicit User Modeling for Personalized
Search’ Information retrieval systems (e.g., web search
engines) are critical for overcoming information overload. A
major lack of existing retrieval systems is that they generally
lack user modeling and are not adaptive to specific users,
resulting in integrally non-optimal retrieval performance. For
example, a tourist and a programmer may use the same word
“java” to search for different data, but the current search
schemes would return the similar results. In this paper, we
study how to gather a user’s interest from the user’s search
context and use the incidental implicit user model for
personalized search. We present a decision theoretic
framework and develop techniques for implicit user modeling
in information retrieval. We develop a smart client-side web
search agent (UCAIR) that can perform ready implicit
feedback, e.g., query development based on previous queries
and immediate result re ranking based on click through
information. Experimentations on web search show that our
search agent can improve search accuracy over the popular
Google search engine.
Zhicheng Dou explain large scale Evaluation and Analysis of
Personalized Search Tactics. Although personalized search has
been proposed for many years and many personalization
tactics have been inspected, it is still unclear whether
personalization is constantly effective on different queries for
different users, and under different search backgrounds. In this
paper, we study this problem and provide some initial
conclusions. We present a large-scale evaluation framework
for personalized search based on query logs, and then evaluate
five personalized search tactics (including two click-based and
three profile-based ones) by 12-day MSN query logs. By
analyzing the results, we reveal that personalized search has
significant improvement over common web search on some
queries but it has little effect on other queries (e.g., queries
with small click entropy). It even harms search accuracy under
some conditions. Furthermore, we show that straightforward
click-based personalization tactics perform steadily and
considerably well, while profile-based ones are unbalanced in
our experiments. We also reveal that both long term and short-
term contexts are very important in improving search
performance for profile-based personalized search strategies.
III. PROBLEM DEFINITION
The proposed system uses the combination of keyword
search (by film, singer, actor or any other) with more intensive
and efficient method of search using text query. The search
method is very simple and optimize the query due to data
input by user and song database. The recommendation
mechanism uses intensive method and is responsible for
making suggestion of songs unknown to user but similar to his
query and positioning search results. This system provides not
only songs result but also videos and movies.
IV. PROPOSED METHODOLOGY
The module split-up shows how proposed system is supposed
to be work. First is the user register the personal information
and then login the form. When user opens Application he/she
entered keyword related song like some keyword of song,
singer name, some word of song. After this stage system
match query result from database with query string given by
user. System shows result on the basis of following aspects:
1. How much predict string match with query file database.
2. How many songs match with that query string?
3. Filter results by; from the list of songs which song is
recommended for the user.
4. Filter result also by, which songs are perfect match with that
query string and so on.
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Fig.1 Proposed Module Split-up
The keyword search based method is very easy and useful to
all people. In proposed system there are two paneling the
admin panel and user panel. In admin panel the admin need to
enter email and password to login and upload songs, videos,
movies and their related details. The proposed system in user
panel first is the registration for new membership. The user
has to enter email address, password and choice. The second
step is sign in to start session and user need to enter email id
and password to login. After the home page is open and the
third step is user to enter keyword for searching song. User
can search from own database and from internet also. Internet
Search (Result – same for all basis): user can also search song
over the internet which gives result same for all basis.
Text search with Recommendation (music): user can search
from his own database. Play song when user clicks on
searched song it plays automatically.
Text search with Recommendation not only songs but also
videos, movies. Play video when user clicks on searched song
it play automatically.
V. ALGORITHM IMPLEMENTATION
NAÏVE BAYES ALGORITHM:
We will propose a new preference mining algorithm,
called Spy Naïve Bayes (SpyNB). It comprises of two main
components: a spying technique to gain more accurate
negative samples and a voting procedure to consider the
opinions of all spies.
According to our click through interpretation, we
need to classify unlabeled data in order to determine the
predicted negative links. Naive Bayes is a simple and efficient
text classification method. However, conventional Naïve
Bayes needs both positive and negative samples as training
data, while we only have positive samples. To address this
problem, we employ a spying method to train Naive Bayes by
integrating unlabeled training examples. Moreover, in order to
obtain more precise predicted negatives, we further present a
voting procedure to make full use of all possible spies. Finally,
we propose our Spy Naive Bayes algorithm.
ALGORITHM 1
Training the Naïve Bayes Algorithm
Input:  1 2 NL l ,l ,.....,l /*a set of links*/
Output: Prior probabilities: Pr(+) and Pr(-)
Likelihoods:
 jPr w | and    1jPr w | j ,.....,M  
ALGORITHM 2
The Spy Naïve Bayes (SpyNB) Algorithm
Input: P → a set of positive examples;
U → a set of unlabeled examples;
vT → a voting threshold;
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4. International Journal of Engineering Science and Computing, May 2016 4598 http://ijesc.org/
Output: PN → the set of predicted negative examples;
By using the above algorithm we will create a user
profile, which will consider both users’ positive preferences
and negative preferences.
ALGORITHM: REVERSE SEARCHING
USE: Reverse searching for more sites from deep
web interfaces.
Input: seed sites and harvested deep websites
Output: relevant sites
1 while # of candidate sites less than a threshold do
2 // pick a deep website
3 site = getDeepWebSite(siteDatabase, seedSites)
4 resultPage = reverseSearch(site)
5 links = extractLinks(resultP age)
6 for each link in links do
7 page = downloadPage(link)
8 relevant = classify(page)
9 if relevant then
10 relevantSites = extractUnvisitedSite(page)
11 Output relevantSites
12 end
13 end
VI. RESULT AND ANALYSIS
We used a publicly available training set of complete music
lyrics, with about 500 songs and a total vocabulary of about
44,000 words. We randomly chose 9/10th of the data for our
training set and used the rest for our test set, on which we
performed Naive Bayes and KNN algorithm. Our results were,
with about 65% accuracy for Naïve Bayes, and about 60%
accuracy for k-NN algorithm. Upon further inspection, we
noticed that our dataset was highly skewed: 55% of the songs
were from the genre “Classic, POP, Hip-Hop and Love.”
Algorithm Naive Bayes
algorithm
KNN Algorithm
Songs 500 500
Training
set
9/10th
of data 9/10th
of data
Time(ms) 90-100ms Near 1000ms
Accuracy 65% 60%
Table 1: Naïve Bayes v/s KNN algorithm
Fig.2 Accuracy v/s Time (ms)
VII. CONCLUSION AND FUTURE SCOPE
The process of query by keyword based retrieval is easy,
simple and natural way of performing music and video search.
We analyzed that, proposed system use any song related
keyword like song name, movie name or singer name which is
efficient method to perform search operation. Time
requirement for searching songs as compare to existing
keyword based search method is efficient.
Proposed system will significantly improve search accuracy
and success rate by using some factors like user search history
and recommendation. In proposed system we used user search
history log and recommendation for predicting more accurate
search results by using Naïve Bayesian classification method.
The search system will caught up by the cold start effect;
when user performs searching for first time which has no past
search history or recommendation is given, it would cause the
accuracy rate remain unimproved. So, we also focused on the
calculation of similarity of keyword given by users for
matching process and improve it by classification of results
using Naïve Bayes algorithm.
Many time it happed that some songs have similar keyword
like user search song or video by using keyword ‘tere’ then
he/she will get results like ‘tere bin’, ‘tere naam’, ‘tere bina’
and so on if he/she does not have any past history or even they
don’t have recommendation and it also happed Because, music
has many keyword variations. This generate problem which is
very often difficult for a system to detect and predict the songs
which user is looking for. So, proposed system use the concept
of recommendation, ranking calculation and user search
history log according to the user profile which provide
prioritized results according to user interested songs and
videos which is first based on user search history and then by
suggestion and recommendation given to that particular user.
Through our experiments, we confirmed that this proposed
method or application can significantly improve the search
accuracy and introduce new searching approach which reduce
users or human search efforts.
In future, we are going to proposed expansion of proposed
search method by considering some more search factors like
search by voice of users using singing and humming i.e.
search song by content based retrieval method for reducing
human typing efforts and search songs which users want but
he/she is unable to type or not even remember the songs
related details like song name, title and lyrics of song but
know only some part of songs which gives new expansion in
searching technology.
VIII. REFERENCES
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ACKNOWLEDGEMENT
The authors would like to thank Mrs. Deepa B. Mane Madam,
professor at Anantrao Pawar college of engineering &
Research, Pune for providing us generous support and
valuable verification on each and every step in terms of
searching songs, videos, movies by using keyword-based
retrieval systems using hybrid recommendation mechanism.
suraj ligade