Mohan Pidugu

To join in a progressive company, where I can pursue a successful career by utilizing my skill and abilities to the maximum extent for the development of the company and myself.

An enthusiastic fresher with highly motivated and leadership skills having MCA with 74.00% Aggregate at SRI VENKATESWARA UNIVERSITY in the year 2018. I completed my under graduation course B.Sc (Maths, Physics, Computers) in SSR Degree College with 68.55% in the year 2015. I completed my Intermediate course M.P.C in SSSR Jr College atmakur, with 77.3% in the year 2012. I completed my SSC in ZPHS Atmakur, with 78.83% in the year 2010.

• Hands on experience in Python and Django Framework and RestAPI
• Eager to learn and adapt to new technologies.
• Ability to produce best result in pressure situation.

• Telugu
• English

A COCKTAIL APPROACH FOR TRAVEL PACKAGE RECOMMENDATION, Indeed, there are many technical and domain difficulties natural in developing and applying an effective recommender system for customized journey program suggestions. First, journey information are much fewer and sparser than conventional products, such as movies for suggestions, because the costs for a journey are much more expensive than for watching a movie. Second, every journey program includes many scenery (places of interest and attractions), and, thus, has implicit complex spatio-temporal relationships. For example, a journey program only includes the scenery which are geographically co-located together. Also, different holiday offers are usually designed for different journey periods. Therefore, the scenery in a journey program usually have spatial temporary auto correlations. Third, conventional recommender systems usually rely on customer precise scores. However, for journey information, the customer scores are usually not ideally available. Lastly, the conventional products for suggestions usually have a long duration of constant value, while the values of holiday offers can easily devalue eventually and a program usually only can last for a certain time interval. The journey companies need to actively tour offers to replace the old ones in accordance with the passions of the visitors. To address these difficulties, in our initial work, we suggested a mixture strategy on customized journey program suggestions. Specifically, we first evaluate the key features of the existing holiday offers. Along this line, journey efforts and holiday destinations are separated into different periods and areas. Then, we develop a tourist-area-season subject (TAST) design, which can signify holiday offers and visitors by different subject withdrawals. In the TAST design, the removal of topics is programmed on both the visitors and the implicit features (i.e., locations, journey seasons) of the scenery. As a result, the TAST design can well signify the content of the holiday offers and the passions of the visitors. Depending on this TAST design, a mixture strategy is designed for customized journey program suggestions by considering some other elements including the periodic actions of visitors, the prices of holiday offers, and the cold start problem of new offers. Lastly, the trial results on real-world journey information show that the TAST design can effectively catch the improvements of journey information and the mixture suggestions strategy works much better than conventional techniques.

I hereby declare that the particulars mentioned in the resume are best of my knowledge and belief.

Windows, Python, Django, RestAPI, JavaScript, HTML, CSS, PyCharm, Eclipse, Python 3.x, Python 2.x, MS-Office

• Father's Name: P CHINNA RAMANAIAH
• Mother's Name: P AUDILAKSHMAMMA