Last week it was time for the third Sitecore Hackathon. Like in the past two years, I have participated in the Team Uniques, together with Reto Hugi and Tobias Studer. We wanted to build a simple recommendation engine: It should recommend items (content) which are similar to the item the user is currently viewing. With this blog post, I want to show how you can easily build a very simple recommendation engine.


Without being mathematicians and studying all the complex mathematics to calculate the “perfect” recommendations, we decided to split the task into two steps: First, all the items should be tagged (with important keywords, depending on the content). Based on these keywords, we search for items with similar keywords.

Item tagging

There are a lot of different ways to tag your content. For demonstrating purposes, the easiest way is to create a new field Keywords on each item and enter pipe-separated keywords into this field, e.g. Sitecore|Content Search|Habitat.

During the Hackathon we wanted to build a solution which works fully automated. So we have created a computed field with the Content Search API and use the Key phrase extraction service of the Azure Machine Learning APIs to get a list of important keywords depending on the content of the Body field on each item. If you want to learn more about this, ping @studert on Twitter.

Similarity algorithm

Calculating the similarity between two documents is done at runtime. As I have already mentioned, this is a very simple algorithm. The Content Search API is an abstraction layer over the underlaying storage (which actually is Lucene or Solr). Lucene would have a great API to calculate similarities between documents, which would perfectly fit our needs. But as we don’t want to rely on Lucene, we needed to go with the abstraction (the Content Search API).

First we need to create a model to get the keywords in a search query (take care of the property Hits, we need this later on):

public class RecommendationSearchResult : SearchResultItem
    public virtual IEnumerable<string> Keywords { get; set; }

    public virtual int Hits { get; set; }

With this model, we first execute a search to get all the items, which have at least one same keyword as the current item. We make use of the PredicateBuilder with a combination of OR-conditions for this:

private IQueryable<RecommendationSearchResult> GetAllMatching(IProviderSearchContext context, IEnumerable<string> keywords, Item item)
    // build the predicate -> Keyword1 OR keyword2 OR keyword 3
    var predicate = PredicateBuilder.False<RecommendationSearchResult>();
    predicate = keywords.Aggregate(predicate, (current, phrase) => current.Or(resultItem => resultItem.Keywords.Contains(phrase)));

    // execute the query, excluding the current item
    return context.GetQueryable<RecommendationSearchResult>()
        .Filter(resultItem => resultItem.ItemId != item.ID)

After this, we enrich the Hits property of each search result with the number of keywords which are identical to the current item. If an item has 4 identical keywords it should be ranked better than one with only 2 identical keywords.

private IEnumerable<RecommendationSearchResult> GetRankedResults(IQueryable<RecommendationSearchResult> searchResults, IEnumerable<string> keywords)
    // calculate number of matchin keywords
    foreach (var searchResult in searchResults)
        searchResult.Hits = searchResult.Phrases.Intersect(keywords).Count();

    // order by number of hits
    return searchResults.OrderByDescending(x => x.Hits);

Last but not least we need to bring this all together and only return the top ranked results:

public IEnumerable<RecommendationSearchResult> GetRecommendations(Item item, int numberOfItems)
    var keywords = item["Keywords"].Split('|');
    using (var context = ContentSearchManager.GetIndex((SitecoreIndexableItem)item).CreateSearchContext())
        var allMatching = this.GetAllMatching(context, keywords, item);
        var rankedResults = this.GetRankedResults(allMatching, keywords);
        var topResults = rankedResults.Take(numberOfItems);

        return topResults.ToList();

As you see, this algorithm is very easy. What it doesn’t consider is the importance and relevance of the keywords. But our tests on Habitat have calculated good recommendations, and we think that it’s even better if there is more content available on a website.


There are a lot of buzzwords available for calculating similarity between two documents, like vector space model, euclidean distance, cosine similarity, token frequency and so on… The algorithm mentioned in this blog post is much simpler and I know it doesn’t calculate “perfect” recommendations. But it turned out that the recommendations are very good too. I think, the more content is available, the better the results are.

Did you ever build such a recommendation engine or do you rely on third party implementation for this? How did you do it and what were your learnings?