Abstract—the personalized web page recommendation is much needed these days. Generally, Web page recommendation systems are implemented in Web servers. They use data implicitly obtained as a collection of Web browsing patterns of the users for recommending webpages. The existing system collects the Web logs and generates a cluster of similar users and recommends pages to the user by actively analyzing it in online.
However the time complexity for analyzing it in online is more. In order to optimize this and to improve the correctness of recommendation systems we propose the method of applying Firefly based algorithm for recommending Web pages along with Naive Bayes clustering. It clusters Web logs in offline using Naive Bayes clustering technique. To find the similarity between the active user query with other users in the cluster Firefly algorithm based similarity measure is used.
The proposed approach uses a probability based clustering which eliminates the odd records while forming clusters. Firefly algorithm meticulously searches the generated web logs present in the cluster of the active user and recommends the top pages. Firefly algorithm utilizes time efficiently, thus it can be used for processing in online. When pages are obtained, they are ranked and the top pages that are more relevant to the query are recommended. The efficiency of the system can be evaluated using measures like precision, recall-Score, Matthews’s correlation and Fallout rate. The proposed approach is expected to improve time utilization in online process as well as recommends more accurate Webpages. Introduction- Web page recommendation system is a sub-domain of recommendation systems that recommends a set of Web pages to the users based on their past browsing patterns. It is done by applying special mining techniques on the data that are previously gathered from the users which in turn discovers and extract information from Web documents and services.
The major concern is about finding the most accurate recommendation algorithms. Recommendation system typically produces the result by following one of the two ways – through collaborative and content based filtering. A. Collaborative Filtering Most recommendation system has wide use of collaborative filtering for recommending items. This method lies on collecting and processing the information’s on user’s behaviors or activities and then predicting the items relating to their similarity with other users. Collaborative filtering approaches building a structure from a user’s past behaviors and decisions of other similar users.
This model is then used to predict items that the user may have an interest in. Since collaborative filtering does not rely on machine analyzable contents, it is capable of recommending for complex items accurately without “understanding” of the item itself. B. Content Based Filtering Content based filtering is another common approach when designing recommendation systems. This technique is based on a definition of the item and a user’s preferred profile. In a content based recommendation systems, the keywords are considered as user’s interest. Content based filtering approaches utilize a series of distinct property of an item in order to obtain and recommend items with same properties.
These approaches are often combined as Hybrid Recommendation Systems. These algorithm try to recommend items based on examining the items that are liked by a user in the past or in the present. In general, various candidate items are compared with items previously rated by the user and the best matching items are recommended. II. Literature survey Recommendation system plays a vital role in recommending personalized items for the users based on their interest in a web services.
The web also contains a rich and dynamic information’s. The amount of information on the web is growing rapidly, as well as the number of web sites and webpages per web site. Predicting the needs of a web user as she visits web sites has gained importance.
Many webpage recommendation system were developed in the past, since they compute recommendations in online process, their time utilization should be efficient. A system 4 that uses support vector machine (SVM) learning based model was developed for computing similarity between two items which performed better than latent factor approach for group recommendations. Since the matrix representation was followed, the data sparsity problem was solved.
However, the system was not able to stably scale when size of the group dynamically increased. Hybrid recommender systems that combines two or more recommendation techniques was designed 5. It eliminates any weakness which exist when only one recommender system is used. There are several ways in which the systems can be combined, such as weighted hybrid recommender where the score of a recommended item is computed from the results of all of the available recommendation techniques present in the system. However, data sparseness was still a problem, the system may generate week recommendations if few users have rated the same items and also the system doesn’t overcome the cold start problem.
Hyperspectral sensors can acquire hundreds ofcontiguous bands over a wide electromagnetic spectrum for each pixel. The rich spectral information allows for distinguishing materials with subtle spectral discrepancy, but it usually leads to the “curse of dimensionality”. To address this, an improved firefly algorithm based band selection method 8 was used. The Firefly algorithm is an evolutionary optimization algorithm proposed by Yang 13. After the initializations of parameters, the brightness is calculated with the objective function (2.
1), where t is the maximum iterations, ? is the step size and ? is the light absorbance of m number of fireflies. The moment states are then evaluated and the bands are selected. In order to avoid employing an actual classifier within the band searching process to greatly reduce computational cost, criterion functions that can gauge class separability are preferred which provided better results. Firefly algorithm also had a faster convergence even at the size of the data is larger To improve the accuracy of similarity measure, a nature inspired algorithm which is based in the behaviour of Fireflies wereintroduced 10.We consider separate effects for ratings of users with similar opinions and conflicting opinions. In order to generate initial population of fireflies, half of population randomly generated and the other half of population are randomly generated. Mean absolute error was chosen as objective function to measure recommendation accuracy which is obtained by difference between predicted rating and real rating. An optimal similarity measure via a simple linear combination of values and ratio of ratings for user-based collaborative filtering provides better results.
It increased speed of finding nearest neighbours of active user and reduce its computation time. Similarity function equation basedon Firefly algorithm was simpler than the equation used in traditional metrics therefore, the proposed methodprovided recommendations faster than traditional metrics. Graph colouring problems are generally discrete. Algorithms to discrete problems are quite complex. A newalgorithm based on Similarity and discretize firefly algorithm directly without any other hybrid algorithm was developed 11. It was adoptable to dynamic graph sizes. A system for assigning an electronic document to one or more predefined categories or classes based on its textual context and use of agglomerative clustering algorithm was developed 6. This type of clustering along with sample correlation coefficient as similarity measure, allowed high indexing term space reduction factor with a gain of higher classification accuracy.
In order to minimize noise and outlier data, a modified DBSCALE algorithm using Naïve Bayes has been designed 7. This algorithm is basically a prospect based utility. This function is used to estimate the outlier cluster data and increase the correctness rate of algorithm on given threshold value.
Since Naïve Bayes is a probability based function, it removes outlier cluster data and increases the correctness rate according to threshold value. It also computes maximum posterior hypothesis for outlier data. In order to minimize noise and outlier data, a modified DBSCALE algorithm using Naïve Bayes has been designed 7. This algorithm is basically a prospect based utility. This function is used to increase the correctness rate of algorithm on given threshold value and to estimate the outlier cluster data. Since Naïve Bayes is a probability based function, it removes outlier cluster data and increases the correctness rate according to threshold value. It also computes maximum posterior hypothesis for outlier data.
The memory based collaborative system uses matrix based computation and solves data sparsity problem but, scalability of the system cannot be stable when size of the group dynamically increases. Hybrid system could be helpful in overcoming the scalability issue but it again leads to cold start problem. To eliminate outliers as well as overcoming other two problems Naive Bayes clustering, a probability based method was used in past. Firefly algorithm has a faster convergence and searches all possible subsets with better time utilization. Thus, to design an efficient recommendation system, Naïve Bayes method can be followed for clustering in offline. Since the time complexity should be less, Firefly algorithm that is more efficient in terms of time utilization, it can be used for calculating similarity in online. Combination of these two technique might increase the accuracy of the recommendation system as well as results in efficient time utilization. III.
Overview of the proposed work Initially, the web log files are obtained from the 1 America Online Inc. The log files consists of five fields i.e. anonymous ID for individual user, query of each user along with query time, list of URLs which user proceeded and its rank in the result.
These logs are collected and grouped based on anonymous ID. The URL among all the users are obtained and its content are downloaded and processed. The processing of data includes removal of stop words from the URL’s data and keyword extraction. Similar users are clustered based on fetched keywords by using Naïve Bayes clustering technique which provides efficient clusters compared to clustering by the use of association rules. The created clusters are given to online component. In online process, when an active user gives a query, the keywords from the query is extracted. The similarity between the extracted keywords with the other users in the same cluster of the active user is calculated using Firefly similarity measure. The similarity values are sorted along with the web pages browsed by similar users in the cluster.
The top k web pages are recommended for the active user as a result. IV. proposed work The proposed system follows a linear process of initially collecting the web logs and processing them followed by clustering similar users by Naïve Bayes clustering technique and finally generating recommendations based on a similarity measure from firefly algorithm. A. Preprocessing of Web Logs The web logs are collected form 1 AOL Inc.
It consists of 20 million web queries from 650 thousand real users over 3 months. The data set includes anonymous ID, query, query time, item rank and click URL. The log file contains many number of users along with the web pages visited by them. It is validated and separated based on anonymous ID. The user is separated into individual file using anonymous ID. The content from the URL are fetched and downloaded.
Those keywords are processed which undergoes stop words removal and stemming process. The final keywords are then extracted. The features like keywords, Timings, Frequency, Click URL and Revisit are fetched. The user profile is constructed using those features. The user profile that constructed is based on the features that are taken form the user log files.
· Timing: The timing that the user spent on that particular URL · Frequency: The amount of time the user visited the URL · Clickstream: The number of click stream that are visited by user · Revisit: Whether the user visited the web page The keywords are generated from the data fetched form the URL. Timing for each URL is estimated from the given date and time by calculating the difference between the each URL that are searched in a single day by having some time constraints. Frequency is hence calculated such that numberof times the user clicked the URL. The clickstreams are those that are clicked by the user for additional information. The timing of revisit is calculated such that to decide whether the user preferred it much or not.
Keywords: Keywords are those which are extracted from the URL. The information from the URL is hence collected and processed to obtain features of the user. B. Naïve Bayes Clustering Clustering, also known as unsupervised classification, is a descriptive task with many applications.
Clustering is decomposition or partition of a data set into groups in such a way that the object in one group are similar to each other but as different as possible from the object in other groups. Three main approach for clustering of data is partition based clustering, hierarchical clustering and probabilistic model based clustering. Probabilistic model based clustering is a soft clustering were an object can belong to more than one cluster following a probability distribution. A clustering is useful if it produces some interesting insight in the problem that we are analysing.
Naïve Bayes clustering is also a probabilistic clustering technique that is based in Bayes theorem with strong independent assumption between features. The feature variables can be discrete or continuous. This probabilistic clustering lies on nominal and numeric variables in the data set and its novelty lies in the use of mixture of truncated exponential (MTE) densities to model the numeric variables. In Naïve Bayes clustering the class is the only root variable and all the attributes are conditionally independent given the class.
The clustering problem reduces to take a data set of instances and a previously specified number of clusters (k), and work out each cluster’s distribution and the population distribution between the clusters. To obtain these parameters the expectation maximization (EM) algorithm is used. Since Naïve Bayes clustering is a probability based techniques. The items belongs to the cluster if and only if it has a relation to it.
This helps in eliminating outlier data in the process of clustering. It also provides proper clustering with less computations. The given dataset is divided into two parts, one for the training and other for testing. For each record in the test and train databases, the distribution of the class variable is computed. According to the obtained distribution, a value for the class variable is simulated and inserted in the corresponding cluster. The log-likelihood of the new model is computed. If it is higher than the initial model, the process is repeated. Otherwise, the process is stopped, obtained clusters are returned.
C. Optimisation Using Firefly Algorithm Firefly algorithm is an evolutionary algorithm that is based on the behaviour of fireflies. Fireflies live in colonies and cooperate for the survival of the colony. Generally, in order to model the behaviour of fireflies, three assumptions will always be considered i.e. all fireflies are homogeneous, Attractiveness of each firefly is related to its level of brightness, rightness of firefly is determined with an exponential objective function.
Each firefly always emits a kind of light that by which attracts other fireflies. The amount of accessed light depends on parameters such as distance and absorption coefficient of the surroundings. The longer the distance the lesser the amount of accessed light will be. Also in surroundings with high light absorption coefficient such as foggy weathers, the intensity of light decreases. The certain issue is that every firefly regardless of its gender has always been attracted to and moved toward the brighter firefly. Firefly has a light intensity of its own. The key concept is, the firefly with low light intensity is always attracted to the firefly with high light intensity.
This concept can be incorporated for calculating similarity. By using firefly based similarity measure unique and distinguished results can be obtained which is a useful feature for ranking. It can deal with highly non- linear, multi-modal optimization problems naturally and efficiently. It does not use velocities, and there is no problem as that associated with velocity in PSO.
The speed of convergence is very high in probability of finding the global optimized answer. It has the flexibility of integration with other optimization techniques to form hybrid tools. It does not require a good initial solution to start its iteration process. Each web pages visited by the user i are considered a firefly.
The number of user visited the particular page is assumed as the light intensity of the firefly. The objective function is formulated based on the frequency and duration. Frequency is calculated as the ratio to the number of visits per page to the average vests of all pages.
The duration is the ratio of duration of page to the total duration of all the pages visited by the user.Thus, the objective function can be defined as in equation 5.1 Interest (i)= 2*Frequency (i)*Duration (i) Frequency (i)+Duration (i) (5.1) The interest of all users in the cluster is calculated. Then the pages to be recommended are found by using page rank algorithm 2 on the obtained result. The results after applying page rank algorithm is given as the recommended web page to the user. D.
5.2.6 Ranking The Web Pages The result, set of web pages obtain should ranked in an order that the user might have higher interest. Thus, they are ranked in a sorted order based on the interest of the active user. The association rule checks the maximum possible combinations which provides more accurate pages.
E. 5.2.7 Recommendation Process The URL that are to be recommended will be identified based on ranking and similarity measure.
The similarity measure is calculated among the users by comparing their similar interest. From the obtained result of pages, page rank algorithm is used to rank the most relevant pages to the user. Thus, resultant URL’s are recommended to the users. Hence the web page that is to be recommended to the user will be more relevant.
The use of Nave Bayes clustering will eliminate the outliers and Firefly based similarity calculation will check all the subsets of the clusters.