In the last two parts of this series, we have been looking at how to design and implement a movie recommendations engine using the MovieLens' 20 million ratings dataset. We have looked at some of the most common and standard techniques out there namely Content based recommendations, Collaborative Filtering and Latent Factor based Matrix Factorization strategy. Clearly CF and MF approaches emerged as the winners due to their accuracy and […]
In the last post, we had started to design a movie recommendation engine using the 20 million ratings dataset available from MovieLens. We started with a Content Based Recommendation approach, where we built a classification/regression model for each user based on the tags and genres assigned to each movie he has rated. The assumption behind this approach is that, the rating that an user has given to a movie depends […]
In this post, we would be looking to design a movie recommendation engine with the MovieLens dataset. We will not be designing the architecture of such a system, but will be looking at different methods by which one can recommend movies to users that minimizes the root mean squared error of the predicted ratings from the actual ratings on a hold out validation dataset.
Problem Statement Solution : This one looks like a very simple problem at first glance but I found it to be quite tricky during implementation. The straightforward solution is to pre-compute the prefix sums S(i), i.e. the sum of all integers from 0 to i-th index for all possible i, and then compute all possible range sums S(i, j), which is the sum of all integers from index i to […]
In the second post of this series we had listed down different vectorization algorithms used in our experiments for representing questions. Representations form the core of our intent clusters, because the assumption is that if a representation algorithm can capture syntactic as well as semantic meaning of the questions well, then if two questions which actually speak of the same intent, will have representations that are very close to each […]
Problem Statement Solution : This is an interesting problem, not because it is difficult or tricky to find an efficient solution, but there are multiple approaches and each approach is dependent on the problem definition and problem test cases. Identifying words where the space between them has been mistakenly omitted is a classic problem is text processing because this is one way by which search engines such as Google provide […]
Problem Statement Solution : Let's try to build a 'bad' solution first. By 'bad', I mean the approach may not be the most optimal but will return correct results every time. One such approach is to list down all possible substrings and count the unique letters in each of them and then take their sum. This approach is perfectly reasonable approach but why it is not optimal ?
In this post we would be looking at designing a social networking site similar to Twitter. Quite obviously we would not be designing every other feature on the site, but the important ones only. The most important feature on Twitter is the Feed (home timeline and profile timeline). The feeds on twitter drives user engagement and thus it needs to be designed in a scalable way such that it can […]
Problem Statement Solution : One approach that uses O(n) extra space, is to store for each node N, the pointer to the nodes with minimum and maximum values in the sub-tree rooted at N. Let's denote the minimum node rooted at N by N.min and the maximum node by N.max. Then for a sub-tree rooted at N, the sub-tree has a "defect", if : N.val < N.left.max.val and/or N.val > N.right.min.val […]
Problem Statement Solution : Observe that one can switch from one route to another route, if both the routes have at-least one stop in common. Starting with the source stop S, there could be multiple possible bus routes R that includes this stop. Thus for every possible route R that include the stop S, do a Breadth First Search to all possible routes R' reachable from this route. R' can […]