I know this thread is dead but I have one other thought if anyone stumbles upon this in the future. I have applied a very basic recommender using association rules is to take a list of transactions in a database, and a list of rules. Build a Trie out of the rule's ordered antecedent. When the Trie is evaluated it should return the consequent and interest metric for each node at that point in th
Forum: The Data Mining / Big Data Forum

There are many types of recommendations that you may want to make. The type that is most obviously applicable to SPMF is called item to item recommendations. You can think of this like cross-sell. If you land on a page for product A recommendation might be for items that sell well with product A. This is what is described in the association rules section of SPMF. Otherwise, to my knowledge, mo
Forum: The Data Mining / Big Data Forum

Philippe thank you so much for your response, I appreciate it. I think there may be a potential to compute what I am looking for based on your response. We know that the Total_Utility = Utility_a + Utility_b +... Utility_n If we treat utility as case volume instead of dollars, and keep track of unit utilities (Utility_a through Utility_n) or unit fractions and support it would be simple to
Forum: The Data Mining / Big Data Forum

Hello, I am using the FCHM Bond algorithm and find the bond measure to be extremely useful. I'm using this bond to identify products that cross-sell well together. One issue I am running into is that this bond is very difficult to translate into expected volumes. For instance consider the case when a customer buys between 4 and 5 boxes of tomatoes for every 1 box of Pizza Dough. Let's say t
Forum: The Data Mining / Big Data Forum

When calling from python it's easiest in my opinion to do something like this: import subprocess # -Xmx1024m specifies to use 1 gb of memory for the java vm. jar_call = f'java -Xmx1024m -jar spmf.jar run {algorithm} {input}.txt {output_file} ' \ f'{param1} {param2}' jar_call = jar_call.split() #This calls the SPMF JAR file subprocess.call(jar_call) Then You can read back
Forum: The Data Mining / Big Data Forum