Time Based Collaborative Recommendation System by using Data Mining Techniques

Recommendation of appropriate product to the specific user is becoming the key to ensuring the continued success of E-commerce. Today, many E-commerce systems adopt various recommendation techniques, e.g., Collaborative Filtering (abbreviated as CF)-based technique and Structural Balance Theory-based Recommendation (i.e., SBT-Rec) technique to realize product item recommendation. Overall, the present CF recommendation and as per suggested SBT can perform very well, if the target user owns similar friends (user-based CF) and Structural Balance Theory-based Recommendation (i.e., SBT-Rec) for we first look for the target user’s dissimilar “enemy” (i.e., antonym of “friend”), and furthermore, we look for the “possible friends” of E-commerce target user, according to “enemy’s enemy is a friend” rule of Structural Balance Theory or the product items purchased and preferred by target user own one or more similar product items (item-based CF). Here both the systems depends on friends and enemies if we are not getting friends or enemies then. So to improve Recommender system we propose a time-aware profile based collaborative Recommendation algorithm. In this algorithm, we will consider only recently submitted ratings and positive reviews to evaluate products quality. Along with this, we propose a novel recommender system in which user will give his requirement about any product as input, and depending on that input we will recommend most appropriate products according to the customer’s requirement and ratings given by other customers. Only recent ratings will be considered by the system. Our proposed system will meet personalized product item recommendation requirements in E-commerce and time-aware rating consideration to evaluate current product quality

Time Based Collaborative Recommendation System by using Data Mining Techniques

Recommendation of appropriate product to the specific user is becoming the key to ensuring the continued success of E-commerce. Today, many E-commerce systems adopt various recommendation techniques, e.g., Collaborative Filtering (abbreviated as CF)-based technique and Structural Balance Theory-based Recommendation (i.e., SBT-Rec) technique to realize product item recommendation. Overall, the present CF recommendation and as per suggested SBT can perform very well, if the target user owns similar friends (user-based CF) and Structural Balance Theory-based Recommendation (i.e., SBT-Rec) for we first look for the target user’s dissimilar “enemy” (i.e., antonym of “friend”), and furthermore, we look for the “possible friends” of E-commerce target user, according to “enemy’s enemy is a friend” rule of Structural Balance Theory or the product items purchased and preferred by target user own one or more similar product items (item-based CF). Here both the systems depends on friends and enemies if we are not getting friends or enemies then. So to improve Recommender system we propose a time-aware profile based collaborative Recommendation algorithm. In this algorithm, we will consider only recently submitted ratings and positive reviews to evaluate products quality. Along with this, we propose a novel recommender system in which user will give his requirement about any product as input, and depending on that input we will recommend most appropriate products according to the customer’s requirement and ratings given by other customers. Only recent ratings will be considered by the system. Our proposed system will meet personalized product item recommendation requirements in E-commerce and time-aware rating consideration to evaluate current product quality

Anomalous densification behavior of Al2O3-Cr2O3 system

Densification behavior of (Al1-xCrx)(2)O-3 (where, x = 0, 0.1, 0.3, 0.5) compacts in the temperature range 1000-1700 degrees C under reducing condition were studied. Up to 1300 degrees C, densification behavior followed the normal trend, i.e., bulk density increases with increase in Cr2O3 content. Anomaly in the densification behavior was observed from 1400 degrees C onwards. Al2O3 samples showed high densification; but the addition of 10 mol%Cr2O3 resulted in a sharp decrease in densification; while further Cr2O3 addition showed a steady rise in densification. XRD studies revealed the solid solution formation starts at 1400 degrees C. A broad endothermic peak at around 1400 degrees C in DTA thermogram corresponds to the formation of solid solution which actually absorbs extra heat energy resulting less dense Al2O3-Cr2O3 compacts. The average grain size of sintered Al2O3-Cr2O3 samples increases with increase in Cr2O3 content. (C) 2015 Elsevier Inc. All rights reserved

Low temperature synthesis of pure anatase carbon doped titanium dioxide: An efficient visible light active photocatalyst

Low temperature pure anatase Carbon Doped Titanium Dioxide (C-TiO2) is successfully synthesized by using starch as an effective, economical, and nonhazardous carbon source. The synthesized C-TiO2 has been further characterized by X-Ray Diffraction, SEM, TEM, BET, XPS and UV- DRS techniques, which reveal that the particles are crystalline with spherical morphology, high surface area and an optical band gap of 2.79 eV for CTiO2 calcined at 400 °C. Furthermore photocatalytic degradation of Rhodamine B dye was carried out using asprepared C-TiO2 under visible light irradiation. Prepared C-TiO2 calcined at 200 °C and 400 °C show higher degradation efficiency (85% and 100% in 120 min respectively) as compared to that of undoped TiO2 and commercial Degussa P-25. Result shows that the C-TiO2 containing lower carbon percentage has higher photocatalytic activity. Thus enhanced photocatalytic activity of C-TiO2, may be due to synergic effect of carbon doping and [101] facet enhanced synthesis of anatase C-TiO2