#Movie suggester movie#
Swipe right to like, swipe left to discard, and swipe up to read more.Īs you progress, the app tweaks its algorithms depending on what you like and soon, you should be able to settle on a TV series or movie in a handful of swipes. Dinggo! (Android, iOS, Web)ĭinggo makes deciding what to watch on Netflix as straightforward as swiping on Tinder.ĭinggo comes with a Tinder-like interface where you can swipe right or left on suggested shows and movies. It supports a range of other streaming platforms including Amazon Prime Video, HBO, Hulu, and more. You can add other Flixi users to your account and explore recommendations that match both you and your friend’s taste.įlixi is not restricted to Netflix either. By keeping tabs on the shows you watch, Flixi continues to adjust that score and recommends stuff you should watch accordingly.Īnother tool on Flixi you’ll find handy is its group compatibility tests. Based on that, it builds what the developer likes to call a Taste Score. Its headlining ability is an AI-based tool that recommends content depending on what you like and what you’ve streamed in the past.Īt launch, Flixi will ask you to rate and add a few TV shows and movies. With an app called Flixi, you can let AI take care of your TV and movie needs.įlixi bills itself as a movie and TV personal assistant. Here are a bunch of apps and websites that will help you figure out what you should watch on Netflix. Browse Netflix’s Secret Categories (Web)įor a few bucks, Netflix puts a sea of content at your disposal but its discovery engine - oblivious to factors such as your mood, your partner’s interests - often leaves you fending for yourself to get through the endless ordeal of picking one.įortunately, there are better solutions you can rely on for Netflix recommendations. The response is ordered by the weights assigned to each movie. Let’s go ahead and query something to see the result we get back: curl -XGET localthost:9092/movies/movies/_search Let’s run the same query that we did a while ago to see if the suggestion results have changed.
> for index, movie in eval(name_year_list): > name_year_list =, movie.split("-")) for movie in movies] Our script to parse this file looks like: > import requests
From a cursory look at the file, it looks like the second and third column in every row of the file are the name and the date of release.Įach line in the file looks something like 1682|Scream of Stone (Schrei aus Stein) (1991)|0||. For this, I am just going to loop through the u.item file from the dataset using a Python script. Now that we created a new movies index and a mapping for the movies index, lets go ahead and add data to the index. curl -XGET localhost:9200/movies/_mapping You can check if the index and mapping was created correctly by running the following command. For now, we will only be using the name of the movie and the year in which the movie was released as a part of our index mapping. Let’s go ahead and define a new mapping for the movies index. Each index has one mapping type which determines how the document will be indexed.To use Completion Suggester, a special type of mapping field type called completion needs to be defined. Defining a Mapping.Ī mapping in Elastic Search is a definition of how a document and its fields are stored. Within the dataset, I am going to be using the u.item file to seed my elastic search cluster with movie data. This data set is publicly available for downloading here "minimum_index_compatibility_version" : "5.0.0"įor this demo, I am going to build auto-complete suggestions on the ml-100k movie dataset. "minimum_wire_compatibility_version" : "5.6.0", "cluster_uuid" : "7QktTxXfSdSEhA5rnONpRw", We can verify that elastic search is running by running the following command curl -XGET localhost:9200 docker run -p 9200:9200 /elasticsearch/elasticsearch:6.0.1 Let’s start with spinning up elastic search. In-order to demo this, I am going to build an auto-complete field mapping on Elastic Search and then eventually build a golang web-service to act as an auto-complete API of sorts. The added advantage is the field can also have fuzziness and weights which means that it can adjust for typos and also re-sort suggestions based on factors that we can decide are more relevant to our use-case. The completion suggester is a search-as-you-type field that is optimized for speed and returns results almost instantaneously. While there are many ways you can go about implementing auto-complete, I prefer using ES’s inbuilt completion suggester because it was built precisely for this use-case. Fast auto-complete can be a pretty big factor in content discovery and a strong implicit funnel to guide users and help them discover your catalog or content.
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